22 datasets found
  1. F

    Mexican Spanish General Conversation Speech Dataset for ASR

    • futurebeeai.com
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    Updated Aug 1, 2022
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    FutureBee AI (2022). Mexican Spanish General Conversation Speech Dataset for ASR [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/general-conversation-spanish-mexico
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Mexico
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Welcome to the Mexican Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Mexican Spanish communication.

    Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Mexican accents and dialects.

    Speech Data

    The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Mexican Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

    •Participant Diversity:
    •
    Speakers: 60 verified native Mexican Spanish speakers from FutureBeeAI’s contributor community.
    •
    Regions: Representing various provinces of Mexico to ensure dialectal diversity and demographic balance.
    •
    Demographics: A balanced gender ratio (60% male, 40% female) with participant ages ranging from 18 to 70 years.
    •Recording Details:
    •
    Conversation Style: Unscripted, spontaneous peer-to-peer dialogues.
    •
    Duration: Each conversation ranges from 15 to 60 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, recorded at 16kHz sample rate.
    •
    Environment: Quiet, echo-free settings with no background noise.

    Topic Diversity

    The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

    •Sample Topics Include:
    •Family & Relationships
    •Food & Recipes
    •Education & Career
    •Healthcare Discussions
    •Social Issues
    •Technology & Gadgets
    •Travel & Local Culture
    •Shopping & Marketplace Experiences, and many more.

    Transcription

    Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

    •Transcription Highlights:
    •Speaker-segmented dialogues
    •Time-coded utterances
    •Non-speech elements (pauses, laughter, etc.)
    •High transcription accuracy, achieved through double QA pass, average WER < 5%

    These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

    Metadata

    The dataset comes with granular metadata for both speakers and recordings:

    •
    Speaker Metadata: Age, gender, accent, dialect, state/province, and participant ID.
    •
    Recording Metadata: Topic, duration, audio format, device type, and sample rate.

    Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

    Usage and Applications

    This dataset is a versatile resource for multiple Spanish speech and language AI applications:

    •
    ASR Development: Train accurate speech-to-text systems for Mexican Spanish.
    •
    Voice Assistants: Build smart assistants capable of understanding natural Mexican conversations.
    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap: 16px;

  2. F

    Spanish (Spain) Call Center Data for Realestate AI

    • futurebeeai.com
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    Updated Aug 1, 2022
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    FutureBee AI (2022). Spanish (Spain) Call Center Data for Realestate AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/realestate-call-center-conversation-spanish-spain
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Spain
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Spanish Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

    Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

    Speech Data

    The dataset features 30 hours of dual-channel call center recordings between native Spanish speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

    •Participant Diversity:
    •
    Speakers: 60 native Spanish speakers from our verified contributor community.
    •
    Regions: Representing different provinces across Spain to ensure accent and dialect variation.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) and age range from 18 to 70.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted agent-customer discussions.
    •
    Call Duration: Average 5–15 minutes per call.
    •
    Audio Format: Stereo WAV, 16-bit, recorded at 8kHz and 16kHz.
    •
    Recording Environment: Captured in noise-free and echo-free conditions.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

    •Inbound Calls:
    •Property Inquiries
    •Rental Availability
    •Renovation Consultation
    •Property Features & Amenities
    •Investment Property Evaluation
    •Ownership History & Legal Info, and more
    •Outbound Calls:
    •New Listing Notifications
    •Post-Purchase Follow-ups
    •Property Recommendations
    •Value Updates
    •Customer Satisfaction Surveys, and others

    Such domain-rich variety ensures model generalization across common real estate support conversations.

    Transcription

    All recordings are accompanied by precise, manually verified transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., background noise, pauses)
    •High transcription accuracy with word error rate below 5% via dual-layer human review.

    These transcriptions streamline ASR and NLP development for Spanish real estate voice applications.

    Metadata

    Detailed metadata accompanies each participant and conversation:

    •
    Participant Metadata: ID, age, gender, location, accent, and dialect.
    •
    Conversation Metadata: Topic, call type, sentiment, sample rate, and technical details.

    This enables smart filtering, dialect-focused model training, and structured dataset exploration.

    Usage and Applications

    This dataset is ideal for voice AI and NLP systems built for the real estate sector:

  3. F

    Spanish(Spain) General Conversation Speech Dataset for ASR

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). Spanish(Spain) General Conversation Speech Dataset for ASR [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/general-conversation-spanish-spain
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Spain
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Welcome to the Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Spanish communication.

    Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Spanish accents and dialects.

    Speech Data

    The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

    •Participant Diversity:
    •
    Speakers: 60 verified native Spanish speakers from FutureBeeAI’s contributor community.
    •
    Regions: Representing various provinces of Spain to ensure dialectal diversity and demographic balance.
    •
    Demographics: A balanced gender ratio (60% male, 40% female) with participant ages ranging from 18 to 70 years.
    •Recording Details:
    •
    Conversation Style: Unscripted, spontaneous peer-to-peer dialogues.
    •
    Duration: Each conversation ranges from 15 to 60 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, recorded at 16kHz sample rate.
    •
    Environment: Quiet, echo-free settings with no background noise.

    Topic Diversity

    The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

    •Sample Topics Include:
    •Family & Relationships
    •Food & Recipes
    •Education & Career
    •Healthcare Discussions
    •Social Issues
    •Technology & Gadgets
    •Travel & Local Culture
    •Shopping & Marketplace Experiences, and many more.

    Transcription

    Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

    •Transcription Highlights:
    •Speaker-segmented dialogues
    •Time-coded utterances
    •Non-speech elements (pauses, laughter, etc.)
    •High transcription accuracy, achieved through double QA pass, average WER < 5%

    These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

    Metadata

    The dataset comes with granular metadata for both speakers and recordings:

    •
    Speaker Metadata: Age, gender, accent, dialect, state/province, and participant ID.
    •
    Recording Metadata: Topic, duration, audio format, device type, and sample rate.

    Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

    Usage and Applications

    This dataset is a versatile resource for multiple Spanish speech and language AI applications:

    •
    ASR Development: Train accurate speech-to-text systems for Spanish.
    •
    Voice Assistants: Build smart assistants capable of understanding natural Spanish conversations.
    <span

  4. F

    Colombian Spanish General Conversation Speech Dataset for ASR

    • futurebeeai.com
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    Updated Aug 1, 2022
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    FutureBee AI (2022). Colombian Spanish General Conversation Speech Dataset for ASR [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/general-conversation-spanish-colombia
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Colombia
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Welcome to the Colombian Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Colombian Spanish communication.

    Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Colombian accents and dialects.

    Speech Data

    The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Colombian Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

    •Participant Diversity:
    •
    Speakers: 60 verified native Colombian Spanish speakers from FutureBeeAI’s contributor community.
    •
    Regions: Representing various provinces of Colombia to ensure dialectal diversity and demographic balance.
    •
    Demographics: A balanced gender ratio (60% male, 40% female) with participant ages ranging from 18 to 70 years.
    •Recording Details:
    •
    Conversation Style: Unscripted, spontaneous peer-to-peer dialogues.
    •
    Duration: Each conversation ranges from 15 to 60 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, recorded at 16kHz sample rate.
    •
    Environment: Quiet, echo-free settings with no background noise.

    Topic Diversity

    The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

    •Sample Topics Include:
    •Family & Relationships
    •Food & Recipes
    •Education & Career
    •Healthcare Discussions
    •Social Issues
    •Technology & Gadgets
    •Travel & Local Culture
    •Shopping & Marketplace Experiences, and many more.

    Transcription

    Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

    •Transcription Highlights:
    •Speaker-segmented dialogues
    •Time-coded utterances
    •Non-speech elements (pauses, laughter, etc.)
    •High transcription accuracy, achieved through double QA pass, average WER < 5%

    These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

    Metadata

    The dataset comes with granular metadata for both speakers and recordings:

    •
    Speaker Metadata: Age, gender, accent, dialect, state/province, and participant ID.
    •
    Recording Metadata: Topic, duration, audio format, device type, and sample rate.

    Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

    Usage and Applications

    This dataset is a versatile resource for multiple Spanish speech and language AI applications:

    •
    ASR Development: Train accurate speech-to-text systems for Colombian Spanish.
    •
    Voice Assistants: Build smart assistants capable of understanding natural Colombian conversations.
    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex;

  5. F

    Spanish Agent-Customer Chat Dataset for Healthcare Domain

    • futurebeeai.com
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    Updated Aug 1, 2022
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    FutureBee AI (2022). Spanish Agent-Customer Chat Dataset for Healthcare Domain [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/spanish-healthcare-domain-conversation-text-dataset
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    The Spanish Healthcare Chat Dataset is a rich collection of over 10,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in Spanish-speaking regions.

    Participant & Chat Overview

    •
    Participants: 150+ native Spanish speakers from the FutureBeeAI Crowd Community
    •
    Conversation Length: 300–700 words per chat
    •
    Turns per Chat: 50–150 dialogue turns across both participants
    •
    Chat Types: Inbound and outbound
    •
    Sentiment Coverage: Positive, neutral, and negative outcomes included

    Topic Diversity

    The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:

    •
    Inbound Chats (Customer-Initiated): Appointment scheduling, new patient registration, surgery and treatment consultations, diet and lifestyle discussions, insurance claim inquiries, lab result follow-ups
    •
    Outbound Chats (Agent-Initiated): Appointment reminders and confirmations, health and wellness program offers, test result notifications, preventive care and vaccination reminders, subscription renewals, risk assessment and eligibility follow-ups

    This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.

    Language Diversity & Realism

    This dataset reflects the natural flow of Spanish healthcare communication and includes:

    •
    Authentic Naming Patterns: Spanish personal names, clinic names, and brands
    •
    Localized Contact Elements: Addresses, emails, phone numbers, and clinic locations in regional Spanish formats
    •
    Time & Currency References: Use of dates, times, numeric expressions, and currency units aligned with Spanish-speaking regions
    •
    Colloquial & Medical Expressions: Local slang, informal speech, and common healthcare-related terminology

    These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.

    Conversational Flow & Structure

    Conversations range from simple inquiries to complex advisory sessions, including:

    •General inquiries
    •Detailed problem-solving
    •Routine status updates
    •Treatment recommendations
    •Support and feedback interactions

    Each conversation typically includes these structural components:

    •Greetings and verification
    •Information gathering
    •Problem definition
    •Solution delivery
    •Closing messages
    •Follow-up and feedback (where applicable)

    This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.

    Data Format & Structure

    Available in JSON, CSV, and TXT formats, each conversation includes:

    •Full message history with clear speaker labels
    •Participant identifiers
    •Metadata (e.g., topic tags, region, sentiment)
    •Compatibility with common NLP and ML pipelines

    Applications

    <p

  6. F

    Mexican Spanish Call Center Data for Telecom AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
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    FutureBee AI (2022). Mexican Spanish Call Center Data for Telecom AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/telecom-call-center-conversation-spanish-mexico
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Mexico
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Mexican Spanish Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

    Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

    •Participant Diversity:
    •
    Speakers: 60 native Mexican Spanish speakers from our verified contributor pool.
    •
    Regions: Representing multiple provinces across Mexico to ensure coverage of various accents and dialects.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) with age distribution from 18 to 70 years.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted interactions between agents and customers.
    •
    Call Duration: Ranges from 5 to 15 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, at 8kHz and 16kHz sample rates.
    •
    Recording Environment: Captured in clean conditions with no echo or background noise.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

    •Inbound Calls:
    •Phone Number Porting
    •Network Connectivity Issues
    •Billing and Payments
    •Technical Support
    •Service Activation
    •International Roaming Enquiry
    •Refund Requests and Billing Adjustments
    •Emergency Service Access, and others
    •Outbound Calls:
    •Welcome Calls & Onboarding
    •Payment Reminders
    •Customer Satisfaction Surveys
    •Technical Updates
    •Service Usage Reviews
    •Network Complaint Status Calls, and more

    This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

    Transcription

    All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., pauses, coughs)
    •High transcription accuracy with word error rate < 5% thanks to dual-layered quality checks.

    These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

    Metadata

    Rich metadata is available for each participant and conversation:

    •
    Participant Metadata: ID, age, gender, accent, dialect, and location.
    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap: 16px;

  7. F

    Spanish (Spain) Call Center Data for Delivery & Logistics AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). Spanish (Spain) Call Center Data for Delivery & Logistics AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/delivery-call-center-conversation-spanish-spain
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Spain
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Spanish Call Center Speech Dataset for the Delivery and Logistics industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking customers. With over 30 hours of real-world, unscripted call center audio, this dataset captures authentic delivery-related conversations essential for training high-performance ASR models.

    Curated by FutureBeeAI, this dataset empowers AI teams, logistics tech providers, and NLP researchers to build accurate, production-ready models for customer support automation in delivery and logistics.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native Spanish speakers. Captured across various delivery and logistics service scenarios, these conversations cover everything from order tracking to missed delivery resolutions offering a rich, real-world training base for AI models.

    •Participant Diversity:
    •
    Speakers: 60 native Spanish speakers from our verified contributor pool.
    •
    Regions: Multiple provinces of Spain for accent and dialect diversity.
    •
    Participant Profile: Balanced gender distribution (60% male, 40% female) with ages ranging from 18 to 70.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted customer-agent dialogues.
    •
    Call Duration: 5 to 15 minutes on average.
    •
    Audio Format: Stereo WAV, 16-bit depth, recorded at 8kHz and 16kHz.
    •
    Recording Environment: Captured in clean, noise-free, echo-free conditions.

    Topic Diversity

    This speech corpus includes both inbound and outbound delivery-related conversations, covering varied outcomes (positive, negative, neutral) to train adaptable voice models.

    •Inbound Calls:
    •Order Tracking
    •Delivery Complaints
    •Undeliverable Addresses
    •Return Process Enquiries
    •Delivery Method Selection
    •Order Modifications, and more
    •Outbound Calls:
    •Delivery Confirmations
    •Subscription Offer Calls
    •Incorrect Address Follow-ups
    •Missed Delivery Notifications
    •Delivery Feedback Surveys
    •Out-of-Stock Alerts, and others

    This comprehensive coverage reflects real-world logistics workflows, helping voice AI systems interpret context and intent with precision.

    Transcription

    All recordings come with high-quality, human-generated verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., pauses, noise)
    •High transcription accuracy with word error rate under 5% via dual-layer quality checks.

    These transcriptions support fast, reliable model development for Spanish voice AI applications in the delivery sector.

    Metadata

    Detailed metadata is included for each participant and conversation:

    •
    Participant Metadata: ID, age, gender, region, accent, dialect.
    •
    Conversation Metadata: Topic, call type, sentiment, sample rate, and technical attributes.

    This metadata aids in training specialized models, filtering demographics, and running advanced analytics.

    Usage and Applications

    This dataset

  8. F

    Spanish (Spain) Call Center Data for Travel AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    Cite
    FutureBee AI (2022). Spanish (Spain) Call Center Data for Travel AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/travel-call-center-conversation-spanish-spain
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Spain
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Spanish Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Spanish -speaking travelers.

    Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

    Speech Data

    The dataset includes 30 hours of dual-channel audio recordings between native Spanish speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

    •Participant Diversity:
    •
    Speakers: 60 native Spanish contributors from our verified pool.
    •
    Regions: Covering multiple Spain provinces to capture accent and dialectal variation.
    •
    Participant Profile: Balanced representation of age (18–70) and gender (60% male, 40% female).
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, spontaneous customer-agent calls.
    •
    Call Duration: Between 5 and 15 minutes per session.
    •
    Audio Format: Stereo WAV, 16-bit depth, at 8kHz and 16kHz.
    •
    Recording Environment: Captured in controlled, noise-free, echo-free settings.

    Topic Diversity

    Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

    •Inbound Calls:
    •Booking Assistance
    •Destination Information
    •Flight Delays or Cancellations
    •Support for Disabled Passengers
    •Health and Safety Travel Inquiries
    •Lost or Delayed Luggage, and more
    •Outbound Calls:
    •Promotional Travel Offers
    •Customer Feedback Surveys
    •Booking Confirmations
    •Flight Rescheduling Alerts
    •Visa Expiry Notifications, and others

    These scenarios help models understand and respond to diverse traveler needs in real-time.

    Transcription

    Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-Stamped Segments
    •Non-speech Markers (e.g., pauses, coughs)
    •High transcription accuracy by dual-layered transcription review ensures word error rate under 5%.

    Metadata

    Extensive metadata enriches each call and speaker for better filtering and AI training:

    •
    Participant Metadata: ID, age, gender, region, accent, and dialect.
    •
    Conversation Metadata: Topic, domain, call type, sentiment, and audio specs.

    Usage and Applications

    This dataset is ideal for a variety of AI use cases in the travel and tourism space:

    •
    ASR Systems: Train Spanish speech-to-text engines for travel platforms.
    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex;

  9. F

    Mexican Spanish Call Center Data for Travel AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    FutureBee AI (2022). Mexican Spanish Call Center Data for Travel AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/travel-call-center-conversation-spanish-mexico
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Mexico
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Mexican Spanish Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Spanish -speaking travelers.

    Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

    Speech Data

    The dataset includes 30 hours of dual-channel audio recordings between native Mexican Spanish speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

    •Participant Diversity:
    •
    Speakers: 60 native Mexican Spanish contributors from our verified pool.
    •
    Regions: Covering multiple Mexico provinces to capture accent and dialectal variation.
    •
    Participant Profile: Balanced representation of age (18–70) and gender (60% male, 40% female).
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, spontaneous customer-agent calls.
    •
    Call Duration: Between 5 and 15 minutes per session.
    •
    Audio Format: Stereo WAV, 16-bit depth, at 8kHz and 16kHz.
    •
    Recording Environment: Captured in controlled, noise-free, echo-free settings.

    Topic Diversity

    Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

    •Inbound Calls:
    •Booking Assistance
    •Destination Information
    •Flight Delays or Cancellations
    •Support for Disabled Passengers
    •Health and Safety Travel Inquiries
    •Lost or Delayed Luggage, and more
    •Outbound Calls:
    •Promotional Travel Offers
    •Customer Feedback Surveys
    •Booking Confirmations
    •Flight Rescheduling Alerts
    •Visa Expiry Notifications, and others

    These scenarios help models understand and respond to diverse traveler needs in real-time.

    Transcription

    Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-Stamped Segments
    •Non-speech Markers (e.g., pauses, coughs)
    •High transcription accuracy by dual-layered transcription review ensures word error rate under 5%.

    Metadata

    Extensive metadata enriches each call and speaker for better filtering and AI training:

    •
    Participant Metadata: ID, age, gender, region, accent, and dialect.
    •
    Conversation Metadata: Topic, domain, call type, sentiment, and audio specs.

    Usage and Applications

    This dataset is ideal for a variety of AI use cases in the travel and tourism space:

    •
    ASR Systems: Train Spanish speech-to-text engines for travel platforms.
    <div style="margin-top:10px; margin-bottom: 10px;

  10. F

    Mexican Spanish Call Center Data for Delivery & Logistics AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    Cite
    FutureBee AI (2022). Mexican Spanish Call Center Data for Delivery & Logistics AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/delivery-call-center-conversation-spanish-mexico
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Mexico
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Mexican Spanish Call Center Speech Dataset for the Delivery and Logistics industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking customers. With over 30 hours of real-world, unscripted call center audio, this dataset captures authentic delivery-related conversations essential for training high-performance ASR models.

    Curated by FutureBeeAI, this dataset empowers AI teams, logistics tech providers, and NLP researchers to build accurate, production-ready models for customer support automation in delivery and logistics.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured across various delivery and logistics service scenarios, these conversations cover everything from order tracking to missed delivery resolutions offering a rich, real-world training base for AI models.

    •Participant Diversity:
    •
    Speakers: 60 native Mexican Spanish speakers from our verified contributor pool.
    •
    Regions: Multiple provinces of Mexico for accent and dialect diversity.
    •
    Participant Profile: Balanced gender distribution (60% male, 40% female) with ages ranging from 18 to 70.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted customer-agent dialogues.
    •
    Call Duration: 5 to 15 minutes on average.
    •
    Audio Format: Stereo WAV, 16-bit depth, recorded at 8kHz and 16kHz.
    •
    Recording Environment: Captured in clean, noise-free, echo-free conditions.

    Topic Diversity

    This speech corpus includes both inbound and outbound delivery-related conversations, covering varied outcomes (positive, negative, neutral) to train adaptable voice models.

    •Inbound Calls:
    •Order Tracking
    •Delivery Complaints
    •Undeliverable Addresses
    •Return Process Enquiries
    •Delivery Method Selection
    •Order Modifications, and more
    •Outbound Calls:
    •Delivery Confirmations
    •Subscription Offer Calls
    •Incorrect Address Follow-ups
    •Missed Delivery Notifications
    •Delivery Feedback Surveys
    •Out-of-Stock Alerts, and others

    This comprehensive coverage reflects real-world logistics workflows, helping voice AI systems interpret context and intent with precision.

    Transcription

    All recordings come with high-quality, human-generated verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., pauses, noise)
    •High transcription accuracy with word error rate under 5% via dual-layer quality checks.

    These transcriptions support fast, reliable model development for Spanish voice AI applications in the delivery sector.

    Metadata

    Detailed metadata is included for each participant and conversation:

    •
    Participant Metadata: ID, age, gender, region, accent, dialect.
    •
    Conversation Metadata: Topic, call type, sentiment, sample rate, and technical attributes.

    This metadata aids in training specialized models, filtering demographics, and running advanced analytics.

    Usage and Applications

    <p

  11. F

    Argentine Spanish Call Center Data for Realestate AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    Cite
    FutureBee AI (2022). Argentine Spanish Call Center Data for Realestate AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/realestate-call-center-conversation-spanish-argentina
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Argentinians Spanish Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

    Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

    Speech Data

    The dataset features 30 hours of dual-channel call center recordings between native Argentinians Spanish speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

    •Participant Diversity:
    •
    Speakers: 60 native Argentinians Spanish speakers from our verified contributor community.
    •
    Regions: Representing different provinces across Argentina to ensure accent and dialect variation.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) and age range from 18 to 70.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted agent-customer discussions.
    •
    Call Duration: Average 5–15 minutes per call.
    •
    Audio Format: Stereo WAV, 16-bit, recorded at 8kHz and 16kHz.
    •
    Recording Environment: Captured in noise-free and echo-free conditions.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

    •Inbound Calls:
    •Property Inquiries
    •Rental Availability
    •Renovation Consultation
    •Property Features & Amenities
    •Investment Property Evaluation
    •Ownership History & Legal Info, and more
    •Outbound Calls:
    •New Listing Notifications
    •Post-Purchase Follow-ups
    •Property Recommendations
    •Value Updates
    •Customer Satisfaction Surveys, and others

    Such domain-rich variety ensures model generalization across common real estate support conversations.

    Transcription

    All recordings are accompanied by precise, manually verified transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., background noise, pauses)
    •High transcription accuracy with word error rate below 5% via dual-layer human review.

    These transcriptions streamline ASR and NLP development for Spanish real estate voice applications.

    Metadata

    Detailed metadata accompanies each participant and conversation:

    •
    Participant Metadata: ID, age, gender, location, accent, and dialect.
    •
    Conversation Metadata: Topic, call type, sentiment, sample rate, and technical details.

    This enables smart filtering, dialect-focused model training, and structured dataset exploration.

    Usage and Applications

    This dataset is ideal for voice AI and NLP systems built for the real estate sector:

    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap:

  12. F

    Mexican Spanish Call Center Data for BFSI AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    Cite
    FutureBee AI (2022). Mexican Spanish Call Center Data for BFSI AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/bfsi-call-center-conversation-spanish-mexico
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Mexico
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Mexican Spanish Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

    Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native Mexican Spanish speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

    •Participant Diversity:
    •
    Speakers: 60 native Mexican Spanish speakers from our verified contributor pool.
    •
    Regions: Representing multiple provinces across Mexico to ensure coverage of various accents and dialects.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) with age distribution from 18 to 70 years.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted interactions between agents and customers.
    •
    Call Duration: Ranges from 5 to 15 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, at 8kHz and 16kHz sample rates.
    •
    Recording Environment: Captured in clean conditions with no echo or background noise.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

    •Inbound Calls:
    •Debit Card Block Request
    •Transaction Disputes
    •Loan Enquiries
    •Credit Card Billing Issues
    •Account Closure & Claims
    •Policy Renewals & Cancellations
    •Retirement & Tax Planning
    •Investment Risk Queries, and more
    •Outbound Calls:
    •Loan & Credit Card Offers
    •Customer Surveys
    •EMI Reminders
    •Policy Upgrades
    •Insurance Follow-ups
    •Investment Opportunity Calls
    •Retirement Planning Reviews, and more

    This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

    Transcription

    All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •30 hours-coded Segments
    •Non-speech Tags (e.g., pauses, background noise)
    •High transcription accuracy with word error rate < 5% due to double-layered quality checks.

    These transcriptions are production-ready, making financial domain model training faster and more accurate.

    Metadata

    Rich metadata is available for each participant and conversation:

    •
    Participant Metadata: ID, age, gender,

  13. F

    Colombian Spanish Call Center Data for Realestate AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    Click to copy link
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    Close
    Cite
    FutureBee AI (2022). Colombian Spanish Call Center Data for Realestate AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/realestate-call-center-conversation-spanish-colombia
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Colombia
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Colombian Spanish Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

    Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

    Speech Data

    The dataset features 30 hours of dual-channel call center recordings between native Colombian Spanish speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

    •Participant Diversity:
    •
    Speakers: 60 native Colombian Spanish speakers from our verified contributor community.
    •
    Regions: Representing different provinces across Colombia to ensure accent and dialect variation.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) and age range from 18 to 70.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted agent-customer discussions.
    •
    Call Duration: Average 5–15 minutes per call.
    •
    Audio Format: Stereo WAV, 16-bit, recorded at 8kHz and 16kHz.
    •
    Recording Environment: Captured in noise-free and echo-free conditions.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

    •Inbound Calls:
    •Property Inquiries
    •Rental Availability
    •Renovation Consultation
    •Property Features & Amenities
    •Investment Property Evaluation
    •Ownership History & Legal Info, and more
    •Outbound Calls:
    •New Listing Notifications
    •Post-Purchase Follow-ups
    •Property Recommendations
    •Value Updates
    •Customer Satisfaction Surveys, and others

    Such domain-rich variety ensures model generalization across common real estate support conversations.

    Transcription

    All recordings are accompanied by precise, manually verified transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., background noise, pauses)
    •High transcription accuracy with word error rate below 5% via dual-layer human review.

    These transcriptions streamline ASR and NLP development for Spanish real estate voice applications.

    Metadata

    Detailed metadata accompanies each participant and conversation:

    •
    Participant Metadata: ID, age, gender, location, accent, and dialect.
    •
    Conversation Metadata: Topic, call type, sentiment, sample rate, and technical details.

    This enables smart filtering, dialect-focused model training, and structured dataset exploration.

    Usage and Applications

    This dataset is ideal for voice AI and NLP systems built for the real estate sector:

    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap: 16px;

  14. F

    Argentine Spanish Call Center Data for Telecom AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    Cite
    FutureBee AI (2022). Argentine Spanish Call Center Data for Telecom AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/telecom-call-center-conversation-spanish-argentina
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Argentinians Spanish Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

    Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native Argentinians Spanish speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

    •Participant Diversity:
    •
    Speakers: 60 native Argentinians Spanish speakers from our verified contributor pool.
    •
    Regions: Representing multiple provinces across Argentina to ensure coverage of various accents and dialects.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) with age distribution from 18 to 70 years.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted interactions between agents and customers.
    •
    Call Duration: Ranges from 5 to 15 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, at 8kHz and 16kHz sample rates.
    •
    Recording Environment: Captured in clean conditions with no echo or background noise.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

    •Inbound Calls:
    •Phone Number Porting
    •Network Connectivity Issues
    •Billing and Payments
    •Technical Support
    •Service Activation
    •International Roaming Enquiry
    •Refund Requests and Billing Adjustments
    •Emergency Service Access, and others
    •Outbound Calls:
    •Welcome Calls & Onboarding
    •Payment Reminders
    •Customer Satisfaction Surveys
    •Technical Updates
    •Service Usage Reviews
    •Network Complaint Status Calls, and more

    This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

    Transcription

    All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., pauses, coughs)
    •High transcription accuracy with word error rate < 5% thanks to dual-layered quality checks.

    These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

    Metadata

    Rich metadata is available for each participant and conversation:

    •
    Participant Metadata: ID, age, gender, accent, dialect, and location.
    <div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex;

  15. F

    US Spanish Call Center Data for BFSI AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
    Share
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    Click to copy link
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    Close
    Cite
    FutureBee AI (2022). US Spanish Call Center Data for BFSI AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/bfsi-call-center-conversation-spanish-usa
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    United States
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This US Spanish Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

    Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native US Spanish speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

    •Participant Diversity:
    •
    Speakers: 60 native US Spanish speakers from our verified contributor pool.
    •
    Regions: Representing multiple provinces across USA to ensure coverage of various accents and dialects.
    •
    Participant Profile: Balanced gender mix (60% male, 40% female) with age distribution from 18 to 70 years.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted interactions between agents and customers.
    •
    Call Duration: Ranges from 5 to 15 minutes.
    •
    Audio Format: Stereo WAV files, 16-bit depth, at 8kHz and 16kHz sample rates.
    •
    Recording Environment: Captured in clean conditions with no echo or background noise.

    Topic Diversity

    This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

    •Inbound Calls:
    •Debit Card Block Request
    •Transaction Disputes
    •Loan Enquiries
    •Credit Card Billing Issues
    •Account Closure & Claims
    •Policy Renewals & Cancellations
    •Retirement & Tax Planning
    •Investment Risk Queries, and more
    •Outbound Calls:
    •Loan & Credit Card Offers
    •Customer Surveys
    •EMI Reminders
    •Policy Upgrades
    •Insurance Follow-ups
    •Investment Opportunity Calls
    •Retirement Planning Reviews, and more

    This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

    Transcription

    All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •30 hours-coded Segments
    •Non-speech Tags (e.g., pauses, background noise)
    •High transcription accuracy with word error rate < 5% due to double-layered quality checks.

    These transcriptions are production-ready, making financial domain model training faster and more accurate.

    Metadata

    Rich metadata is available for each participant and conversation:

    •
    Participant Metadata: ID, age, gender, accent, dialect, and

  16. F

    Mexican Spanish TTS Speech Dataset for Speech Synthesis

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
    Share
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    Close
    Cite
    FutureBee AI (2022). Mexican Spanish TTS Speech Dataset for Speech Synthesis [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/tts-monolgue-spanish-mexico
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    The Spanish TTS Monologue Speech Dataset is a professionally curated resource built to train realistic, expressive, and production-grade text-to-speech (TTS) systems. It contains studio-recorded long-form speech by trained native Spanish voice artists, each contributing 1 to 2 hours of clean, uninterrupted monologue audio.

    Unlike typical prompt-based datasets with short, isolated phrases, this collection features long-form, topic-driven monologues that mirror natural human narration. It includes content types that are directly useful for real-world applications, like audiobook-style storytelling, educational lectures, health advisories, product explainers, digital how-tos, formal announcements, and more.

    All recordings are captured in professional studios using high-end equipment and under the guidance of experienced voice directors.

    Recording & Audio Quality

    •
    Audio Format: WAV, 48 kHz, available in 16-bit, 24-bit, and 32-bit depth
    •
    SNR: Minimum 30 dB
    •
    Channel: Mono
    •
    Recording Duration: 20-30 minutes
    •
    Recording Environment: Studio-controlled, acoustically treated rooms
    •
    Per Speaker Volume: 1–2 hours of speech per artist
    •
    Quality Control: Each file is reviewed and cleaned for common acoustic issues, including: reverberation, lip smacks, mouth clicks, thumping, hissing, plosives, sibilance, background noise, static interference, clipping, and other artifacts.

    Only clean, production-grade audio makes it into the final dataset.

    Voice Artist Selection

    All voice artists are native Spanish speakers with professional training or prior experience in narration. We ensure a diverse pool in terms of age, gender, and region to bring a balanced and rich vocal dataset.

    •Artist Profile:
    •Gender: Male and Female
    •Age Range: 20–60 years
    •Regions: Native Spanish-speaking states from Mexico
    •
    Selection Process: All artists are screened, onboarded, and sample-approved using FutureBeeAI’s proprietary Yugo platform.

    Script Quality & Coverage

    Scripts are not generic or repetitive. Scripts are professionally authored by domain experts to reflect real-world use cases. They avoid redundancy and include modern vocabulary, emotional range, and phonetically rich sentence structures.

    •
    Word Count per Script: 3,000–5,000 words per 30-minute session
    •Content Types:
    •Storytelling
    •Script and book reading
    •Informational explainers
    •Government service instructions
    •E-commerce tutorials
    •Motivational content
    •Health & wellness guides
    •Education & career advice
    •
    Linguistic Design: Balanced punctuation, emotional range, modern syntax, and vocabulary diversity

    Transcripts & Alignment

    While the script is used during the recording, we also provide post-recording updates to ensure the transcript reflects the final spoken audio. Minor edits are made to adjust for skipped or rephrased words.

    •
    Segmentation: Time-stamped at the sentence level, aligned to actual spoken delivery
    •
    Format: Available in plain text and JSON
    •Post-processing:
    •Corrected for

  17. F

    Argentine Spanish Call Center Data for Delivery & Logistics AI

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
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    FutureBee AI (2022). Argentine Spanish Call Center Data for Delivery & Logistics AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/delivery-call-center-conversation-spanish-argentina
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    This Argentinians Spanish Call Center Speech Dataset for the Delivery and Logistics industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking customers. With over 30 hours of real-world, unscripted call center audio, this dataset captures authentic delivery-related conversations essential for training high-performance ASR models.

    Curated by FutureBeeAI, this dataset empowers AI teams, logistics tech providers, and NLP researchers to build accurate, production-ready models for customer support automation in delivery and logistics.

    Speech Data

    The dataset contains 30 hours of dual-channel call center recordings between native Argentinians Spanish speakers. Captured across various delivery and logistics service scenarios, these conversations cover everything from order tracking to missed delivery resolutions offering a rich, real-world training base for AI models.

    •Participant Diversity:
    •
    Speakers: 60 native Argentinians Spanish speakers from our verified contributor pool.
    •
    Regions: Multiple provinces of Argentina for accent and dialect diversity.
    •
    Participant Profile: Balanced gender distribution (60% male, 40% female) with ages ranging from 18 to 70.
    •Recording Details:
    •
    Conversation Nature: Naturally flowing, unscripted customer-agent dialogues.
    •
    Call Duration: 5 to 15 minutes on average.
    •
    Audio Format: Stereo WAV, 16-bit depth, recorded at 8kHz and 16kHz.
    •
    Recording Environment: Captured in clean, noise-free, echo-free conditions.

    Topic Diversity

    This speech corpus includes both inbound and outbound delivery-related conversations, covering varied outcomes (positive, negative, neutral) to train adaptable voice models.

    •Inbound Calls:
    •Order Tracking
    •Delivery Complaints
    •Undeliverable Addresses
    •Return Process Enquiries
    •Delivery Method Selection
    •Order Modifications, and more
    •Outbound Calls:
    •Delivery Confirmations
    •Subscription Offer Calls
    •Incorrect Address Follow-ups
    •Missed Delivery Notifications
    •Delivery Feedback Surveys
    •Out-of-Stock Alerts, and others

    This comprehensive coverage reflects real-world logistics workflows, helping voice AI systems interpret context and intent with precision.

    Transcription

    All recordings come with high-quality, human-generated verbatim transcriptions in JSON format.

    •Transcription Includes:
    •Speaker-Segmented Dialogues
    •Time-coded Segments
    •Non-speech Tags (e.g., pauses, noise)
    •High transcription accuracy with word error rate under 5% via dual-layer quality checks.

    These transcriptions support fast, reliable model development for Spanish voice AI applications in the delivery sector.

    Metadata

    Detailed metadata is included for each participant and conversation:

    •
    Participant Metadata: ID, age, gender, region, accent, dialect.
    •
    Conversation Metadata: Topic, call type, sentiment, sample rate, and technical attributes.

    This metadata aids in training specialized models, filtering demographics, and running advanced analytics.

    Usage and

  18. F

    Spanish (Spain) Scripted Monologue Speech Data for Telecom

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). Spanish (Spain) Scripted Monologue Speech Data for Telecom [Dataset]. https://www.futurebeeai.com/dataset/monologue-speech-dataset/telecom-scripted-speech-monologues-spanish-spain
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Area covered
    Spain
    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    Presenting the Spanish Scripted Monologue Speech Dataset for the Telecom Domain, a purpose-built dataset created to accelerate the development of Spanish speech recognition and voice AI models specifically tailored for the telecommunications industry.

    Speech Data

    This dataset includes over 6,000 high-quality scripted prompt recordings in Spanish, representing real-world telecom customer service scenarios. It’s designed to support the training of speech-based AI systems used in call centers, virtual agents, and voice-powered support tools.

    •Participant Diversity
    •
    Speakers: 60 native Spanish speakers
    •
    Geographic Distribution: Carefully selected from multiple regions across Spain to capture a wide spectrum of dialects and speaking styles
    •
    Demographics: Balanced representation of males and females (60:40 ratio), aged between 18 to 70 years
    •Recording Specifications
    •
    Type: Scripted monologue prompts focused on telecom industry use cases
    •
    Duration: Each audio clip ranges from 5 to 30 seconds
    •
    Format: WAV files in mono, 16-bit depth, with sample rates of 8 kHz and 16 kHz
    •
    Environment: Clean, echo-free, and noise-controlled settings to ensure optimal audio clarity

    Topic Coverage

    The dataset reflects a wide variety of common telecom customer interactions, including:

    •Customer onboarding and service inquiries
    •Billing and payment questions
    •Data plans and product information
    •Technical support requests
    •Network coverage discussions
    •Regulatory compliance and policy information
    •Upgrades, renewals, and service plan changes
    •Domain-specific scripted interactions tailored to real-world telecom use cases

    Contextual Depth

    To maximize contextual richness, prompts include:

    •
    Localized Names: Common Spain names in various formats
    •
    Addresses: Region-specific address structures for realism
    •
    Dates & Times: Spoken date and time references in typical telecom scenarios (e.g., billing cycles, service activation times)
    •
    Telecom Terminology: Keywords related to mobile data, network, SIM, devices, plans, etc.
    •
    Numbers & Rates: Usage statistics, pricing info, recharge values, and billing figures
    •
    Service Providers: References to telecom companies and third-party service entities

    Transcription

    Each audio file is paired with an accurate, verbatim transcription for precise model training:

    •
    Content: Transcriptions are direct representations of each recorded prompt
    •
    Format: Plain text (.TXT), with filenames matching their corresponding audio files
    •
    Verification: Every transcription is manually verified by native Spanish linguists to ensure consistency and accuracy

    Metadata

    Detailed metadata is included to enhance dataset usability and

  19. F

    Spanish Agent-Customer Chat Dataset for Retail & E-Commerce

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    + more versions
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    FutureBee AI (2022). Spanish Agent-Customer Chat Dataset for Retail & E-Commerce [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/spanish-retail-domain-conversation-text-dataset
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    The Spanish Retail & E-Commerce Chat Dataset is a large-scale, high-quality collection of over 10,000 chat conversations between customers and call center agents, focused exclusively on Retail and E-Commerce domains. Designed to reflect real-world service interactions, this dataset supports the development of robust conversational AI and NLP models tailored for Spanish-speaking audiences.

    Participant & Chat Overview

    •
    Contributors: 150 native Spanish speakers from the FutureBeeAI Crowd Community
    •
    Chat Length: 300–700 words per conversation
    •
    Turn Count: 50–150 dialogue turns across both participants
    •
    Chat Types: Inbound and outbound
    •
    Sentiment Coverage: Positive, neutral, and negative interaction outcomes

    Topic Diversity

    This dataset spans a wide range of Retail and E-Commerce conversation types:

    •Inbound Chats (Customer-Initiated)
    •Product inquiries
    •Return or exchange requests
    •Order cancellations
    •Refunds and payment issues
    •Membership or subscription queries
    •Shipping, delivery, and more
    •Outbound Chats (Agent-Initiated)
    •Order confirmation and verification
    •Cross-selling and upselling
    •Loyalty program promotions
    •Account updates
    •Special offers and discounts
    •Customer feedback and verification

    This diversity enables training of models that handle varied intents, scenarios, and outcomes within customer service workflows.

    Language Nuance & Realism

    The dataset is rich in linguistic diversity and mirrors real conversational tone and structure used in Spanish-speaking regions:

    •
    Personal & Brand Names: Culturally accurate naming conventions
    •
    Local Elements: Realistic addresses, phone numbers, emails, currency references, and time/date formats
    •
    Slang & Idioms: Local expressions, informal phrases, and customer service jargon
    •
    Cultural Specificity: Region-aware vocabulary and tone

    This linguistic authenticity ensures the development of culturally fluent AI models for Spanish Retail & E-Commerce use cases.

    Conversational Structure & Flow

    The conversations reflect natural dialogue dynamics and are organized into various types of interaction styles:

    •Simple inquiries
    •Detailed problem-solving discussions
    •Transactional exchanges
    •Follow-ups and status updates
    •Advisory and assistance sessions

    Each conversation includes common dialogue stages such as:

    •Greetings
    •Customer authentication
    •Information gathering
    <div style="margin-top:10px; margin-bottom: 10px; margin-left: 30px;font-weight: 300; display: flex;

  20. F

    Spanish Agent-Customer Chat Dataset for Delivery & Logistics

    • futurebeeai.com
    wav
    Updated Aug 1, 2022
    Share
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    FutureBee AI (2022). Spanish Agent-Customer Chat Dataset for Delivery & Logistics [Dataset]. https://www.futurebeeai.com/dataset/text-dataset/spanish-delivery-domain-conversation-text-dataset
    Explore at:
    wavAvailable download formats
    Dataset updated
    Aug 1, 2022
    Dataset provided by
    FutureBeeAI
    Authors
    FutureBee AI
    License

    https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

    Dataset funded by
    FutureBeeAI
    Description

    Introduction

    The Spanish Delivery & Logistics Chat Dataset is a comprehensive collection of over 10,000 text-based conversations between customers and call center agents. Focused on real-world delivery and logistics interactions, this dataset captures the language, tone, and service patterns essential for developing robust Spanish-language conversational AI, chatbots, and NLP systems across the delivery ecosystem.

    Participant & Chat Overview

    •
    Participants: 150+ native Spanish speakers from the FutureBeeAI Crowd Community
    •
    Conversation Length: 300–700 words per chat
    •
    Turns per Chat: 50–150 dialogue turns between customer and agent
    •
    Chat Types: Inbound (customer-initiated) and outbound (agent-initiated)
    •
    Sentiment Coverage: Includes positive, neutral, and negative interaction outcomes

    Topic Diversity

    The dataset spans a wide range of delivery and logistics scenarios, ensuring strong coverage across customer service and operational workflows.

    •Inbound Chats (Customer-Initiated)
    •Order tracking and delivery status inquiries
    •Complaints about late or missing deliveries
    •Undeliverable or incorrect address resolution
    •Return process and pickup scheduling
    •Order modifications and change requests
    •Enquiries about delivery method options
    •Outbound Chats (Agent-Initiated)
    •Delivery confirmations and dispatch updates
    •Subscription renewal or delivery reminders
    •Notification of delivery issues or missed attempts
    •Out-of-stock or product unavailability alerts
    •Satisfaction surveys and service feedback collection
    •Address verification for upcoming deliveries

    This topical spread ensures wide applicability in both customer support automation and logistics optimization use cases.

    Language Diversity & Realism

    The conversations reflect the authentic language and interaction style of Spanish-speaking customers and delivery agents, incorporating:

    •
    Naming Patterns: Personal names, business names, and logistics company references
    •
    Localized Details: Spanish-format emails, phone numbers, regional addresses, and delivery zones
    •
    Temporal and Numeric Expressions: Dates, delivery windows, prices, and tracking IDs in Spanish formats
    •
    Slang and Informal Speech: Everyday expressions and delivery-specific idioms used across Spanish dialects

    This linguistic realism enables the development of context-aware and naturally responsive AI systems.

    Conversational Structure & Flow

    The dataset captures a diverse range of interaction types and delivery workflows:

    •Dialogue Types:
    •Quick status checks and confirmations
    •Multi-turn issue resolution
    •Process walkthroughs and guidance
    •Feedback and escalation handling
    •Common Flow Elements:
    •Greetings and caller verification
    •Request or complaint initiation
    <div style="margin-left: 60px;

Share
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Click to copy link
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Close
Cite
FutureBee AI (2022). Mexican Spanish General Conversation Speech Dataset for ASR [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/general-conversation-spanish-mexico

Mexican Spanish General Conversation Speech Dataset for ASR

Mexican Spanish General Conversation Speech Corpus

Explore at:
wavAvailable download formats
Dataset updated
Aug 1, 2022
Dataset provided by
FutureBeeAI
Authors
FutureBee AI
License

https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement

Area covered
Mexico
Dataset funded by
FutureBeeAI
Description

Introduction

Welcome to the Mexican Spanish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Spanish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Mexican Spanish communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Spanish speech models that understand and respond to authentic Mexican accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Mexican Spanish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

•Participant Diversity:
•
Speakers: 60 verified native Mexican Spanish speakers from FutureBeeAI’s contributor community.
•
Regions: Representing various provinces of Mexico to ensure dialectal diversity and demographic balance.
•
Demographics: A balanced gender ratio (60% male, 40% female) with participant ages ranging from 18 to 70 years.
•Recording Details:
•
Conversation Style: Unscripted, spontaneous peer-to-peer dialogues.
•
Duration: Each conversation ranges from 15 to 60 minutes.
•
Audio Format: Stereo WAV files, 16-bit depth, recorded at 16kHz sample rate.
•
Environment: Quiet, echo-free settings with no background noise.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

•Sample Topics Include:
•Family & Relationships
•Food & Recipes
•Education & Career
•Healthcare Discussions
•Social Issues
•Technology & Gadgets
•Travel & Local Culture
•Shopping & Marketplace Experiences, and many more.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

•Transcription Highlights:
•Speaker-segmented dialogues
•Time-coded utterances
•Non-speech elements (pauses, laughter, etc.)
•High transcription accuracy, achieved through double QA pass, average WER < 5%

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

•
Speaker Metadata: Age, gender, accent, dialect, state/province, and participant ID.
•
Recording Metadata: Topic, duration, audio format, device type, and sample rate.

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Spanish speech and language AI applications:

•
ASR Development: Train accurate speech-to-text systems for Mexican Spanish.
•
Voice Assistants: Build smart assistants capable of understanding natural Mexican conversations.
<div style="margin-top:10px; margin-bottom: 10px; padding-left: 30px; display: flex; gap: 16px;

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