Introduction

This UK English 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 English-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 UK English 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.

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.

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.

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:

Update Frequency: N/A

A log of the Unified Call Center's service requests.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Introduction

This US English 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 English-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 English 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.

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.

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.

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

Metadata

Rich metadata is available for each participant and conversation:

Discover our English call center dataset designed for phone service support. Perfect for speech analysis, AI training, and upgrading customer service systems.

Introduction

This US English Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English speakers. Featuring over 30 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native US English speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

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

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

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

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

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

This dataset is ideal for a range of voice AI and NLP applications:

Introduction

This Spanish Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Spanish speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Spanish speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

ArXiv Paper Publication Here: "Real-World En Call Center Transcripts Dataset with PII Redaction" This dataset includes 91,706 high-quality transcriptions corresponding to approximately 10,500 hours of real-world call center conversations in English, collected across various industries and global regions. The dataset features both inbound and outbound calls and spans multiple accents, including Indian, American, and Filipino English. All transcripts have been carefully redacted for PII and… See the full description on the dataset page: https://huggingface.co/datasets/AIxBlock/92k-real-world-call-center-scripts-english.

Introduction

This Hindi 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 Hindi -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 Hindi 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.

Topic Diversity

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

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.

Metadata

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

Usage and Applications

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

Introduction

This Indian English 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 English -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 Indian English 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.

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.

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.

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

Metadata

Detailed metadata accompanies each participant and conversation:

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:

Introduction

This US English 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 English-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 US English 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.

Topic Diversity

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

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.

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

Metadata

Detailed metadata is included for each participant and conversation:

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

Usage and Applications

Introduction

This Filipino 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 Filipino-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 Filipino 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.

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.

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.

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:

Introduction

This US Spanish Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish speakers. Featuring over 30 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native US Spanish speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

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

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

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

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

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

This dataset is ideal for a range of voice AI and NLP applications:

Introduction

This Mandarin Chinese Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Mandarin speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Mandarin Chinese speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.

A large-scale reference dataset for bioacoustics Please find the accompanying code at our official repository: github.com/livingingroups/animal2vec [Optional ]You can find the animal2vec model weights using the MeerKAT dataset here. MeerKAT is a 1068h large-scale dataset containing data from boom-mics and audio-recording collars worn by free-ranging meerkats (Suricata suricatta) at the Kalahari Research Centre, South Africa, of which 184h are labeled with twelve time-resolved vocalization-type ground truth target classes, each with millisecond resolution. The labeled 184h MeerKAT subset exhibits realistic sparsity conditions for a bioacoustic dataset (96% background-noise or other signals and 4% vocalizations), dispersed across 66398 10-second samples, spanning 251562 labeled events and showcasing significant spectral and temporal variability, making it a large-scale reference point with real-world conditions for benchmarking pretraining and finetuning approaches in bioacoustics deep learning. The majority of the audio originates from acoustic collars (Edic Mini Tiny+ A77, Zelenograd, Russia, which sample at 8kHz with 10bit quantization) that were attached to the animals (41 individuals throughout both campaigns), where each file corresponds to a recording for a single individual and day. The remainder of the dataset was recorded using Marantz PMD661 digital recorders (Carlsbad, CA, U.S.) attached to directional Sennheiser ME66 microphones (Wedemark, Germany) sampling at 48kHz with 32bit quantization. When recording, field researchers held the microphones close to the animals (within 1m). The data were recorded during times when meerkats typically forage for food by digging in the ground for small prey. See our paper and [1] and [2] for more details. MeerKAT is released as 384 592 10-second samples, amounting to 1068 h, where 66 398 10-second samples (184 h) are labeled and ground-truth-complete; all call and recurring anthropogenic events in this 184 h are labeled. For further details, see [2]. All samples have been standardized to a sample rate of 8 kHz with 16-bit quantization, sufficient to capture the majority of meerkat vocalization frequencies (the first two formants are below the Nyquist frequency of 4 kHz). The total dataset size of 59 GB (61 GB, including the label files) is comparatively small, making MeerKAT easily accessible and portable despite its extensive length. Each 10-second file has an accompanying HDF5 label file that lists label categories, start and end time offsets (s), and a "focal" designation indicating whether the call was given by the collar-wearing or followed individual or not. By agreement with the Kalahari Research Centre (KRC), we have made these data available in a way that can further machine learning research without compromising the ability of the KRC to continue conducting valuable ecological research on these data. Consequently, the filenames of the 10-second samples have been randomly sampled, and their temporal order and individual identity cannot be recovered, but can be requested from us. [1] Demartsev, V. et al. Signalling in groups: New tools for the integration of animal communication and collective movement. Methods Ecol. Evol. (2022). [2] Demartsev, V. et al. Mapping vocal interactions in space and time differentiates signal broadcast versus signal exchange in meerkat groups. Philos. Trans. R. Soc. Lond. B Biol. Sci. 379 (2024)

Content Analytics Market Outlook

According to our latest research, the global content analytics market size reached USD 7.2 billion in 2024, demonstrating robust momentum driven by the rapid digitization of content across industries. The market is projected to expand at a CAGR of 18.4% from 2025 to 2033, with the market size anticipated to reach USD 35.8 billion by 2033. This impressive growth trajectory is primarily fueled by the increasing demand for actionable insights from unstructured data, advancements in artificial intelligence and machine learning, and the proliferation of digital channels that generate massive volumes of content. As organizations strive to harness the power of data-driven decision-making, content analytics solutions have become indispensable across sectors.

One of the principal growth factors propelling the content analytics market is the exponential surge in digital content creation and consumption. With enterprises and consumers generating vast amounts of data through emails, social media, websites, and multimedia platforms, the need to analyze and extract meaningful patterns from this content has never been greater. Content analytics tools enable organizations to derive valuable business intelligence, optimize marketing strategies, enhance customer experiences, and ensure regulatory compliance. This trend is further amplified by the integration of advanced technologies such as natural language processing (NLP), sentiment analysis, and machine learning, which facilitate deeper and more nuanced understanding of text, audio, and video content. As a result, businesses are increasingly investing in content analytics to gain a competitive edge, streamline operations, and foster innovation.

Another significant factor driving market growth is the rising adoption of cloud-based content analytics solutions. Cloud deployment offers unparalleled scalability, flexibility, and cost-efficiency, making it an attractive choice for organizations of all sizes. The cloud model enables seamless integration with existing IT infrastructure, real-time access to analytics, and the ability to handle large-scale data processing without the need for significant upfront investments in hardware. Additionally, the shift towards remote and hybrid work models has accelerated the demand for cloud-based analytics tools that facilitate collaboration and decision-making across geographically dispersed teams. This transition is particularly pronounced among small and medium enterprises (SMEs), which benefit from the lower total cost of ownership and faster deployment cycles offered by cloud solutions.

The growing emphasis on customer-centric strategies and personalized experiences is also shaping the content analytics market landscape. Organizations across sectors such as retail, BFSI, healthcare, and media are leveraging content analytics to gain deeper insights into customer preferences, behaviors, and feedback. By analyzing data from multiple touchpoints—including social media, customer reviews, and call center transcripts—companies can tailor their offerings, improve engagement, and drive customer loyalty. Furthermore, regulatory requirements around data privacy and security are prompting enterprises to adopt robust analytics solutions that ensure compliance while maximizing the value of their content assets. The convergence of these factors is expected to sustain the strong growth trajectory of the content analytics market in the coming years.

From a regional perspective, North America continues to dominate the global content analytics market, accounting for the largest share in 2024. The region's leadership is attributed to the presence of major technology players, high digital adoption rates, and a mature analytics ecosystem. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, expanding internet penetration, and increasing investments in big data and analytics technologies. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, fueled by rising awareness of the benefits of content analytics and the need to enhance business agility in a dynamic digital landscape.

Dataset Card for "sales-conversations"

This dataset was created for the purpose of training a sales agent chatbot that can convince people. The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644 gpt-3.5-turbo was used for the generation

  Structure

The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc. The customer always starts the conversation Who ends the… See the full description on the dataset page: https://huggingface.co/datasets/goendalf666/sales-conversations.