Introduction

Welcome to the Indian English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English 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 Indian English 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 English speech models that understand and respond to authentic Indian accents and dialects.

Speech Data

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

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.

Transcription

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

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:

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 English speech and language AI applications:

Introduction

Welcome to the Indian English Language Visual Speech Dataset! This dataset is a collection of diverse, single-person unscripted spoken videos supporting research in visual speech recognition, emotion detection, and multimodal communication.

Dataset Content

This visual speech dataset contains 1000 videos in Indian English language each paired with a corresponding high-fidelity audio track. Each participant is answering a specific question in a video in an unscripted and spontaneous nature.

Video Data

While recording each video extensive guidelines are kept in mind to maintain the quality and diversity.

Metadata

The dataset provides comprehensive metadata for each video recording and participant:

Description: The "Indian Languages Audio Dataset" is a collection of audio samples featuring a diverse set of 10 Indian languages. Each audio sample in this dataset is precisely 5 seconds in duration and is provided in MP3 format. It is important to note that this dataset is a subset of a larger collection known as the "Audio Dataset with 10 Indian Languages." The source of these audio samples is regional videos freely available on YouTube, and none of the audio samples or source videos are owned by the dataset creator.

Languages Included: 1. Bengali 2. Gujarati 3. Hindi 4. Kannada 5. Malayalam 6. Marathi 7. Punjabi 8. Tamil 9. Telugu 10. Urdu

This dataset offers a valuable resource for researchers, linguists, and machine learning enthusiasts who are interested in studying and analyzing the phonetics, accents, and linguistic characteristics of the Indian subcontinent. It is a representative sample of the linguistic diversity present in India, encompassing a wide array of languages and dialects. Researchers and developers are encouraged to explore this dataset to build applications or conduct research related to speech recognition, language identification, and other audio processing tasks.

Additionally, the dataset is not limited to these 10 languages and has the potential for expansion. Given the dynamic nature of language use in India, this dataset can serve as a foundation for future data collection efforts involving additional Indian languages and dialects.

Access to the "Indian Multilingual Audio Dataset - 10 Languages" is provided with the understanding that users will comply with applicable copyright and licensing restrictions. If users plan to extend this dataset or use it for commercial purposes, it is essential to seek proper permissions and adhere to relevant copyright and licensing regulations.

By utilizing this dataset responsibly and ethically, users can contribute to the advancement of language technology and research, ultimately benefiting language preservation, speech recognition, and cross-cultural communication.

Introduction

This Indian English 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 English -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 Indian English 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:

This video and gloss-based dataset has been meticulously crafted to enhance the precision and resilience of ISL (Indian Sign Language) gesture recognition and generation systems. Our goal in sharing this dataset is to contribute to the research community, providing a valuable resource for fellow researchers to explore and innovate in the realm of sign language recognition and generation.Overview of the Dataset: Comprising a diverse array of ISL gesture videos and gloss datasets. The term "gloss" in this context often refers to a written or spoken description of the meaning of a sign, allowing for the representation of sign language in a written form. The dataset includes information about the corresponding spoken or written language and the gloss for each sign. Key components of a sign language gloss dataset include ISL grammar that follows a layered approach, incorporating specific spatial indices for tense and a lexicon with compounds. It follows a unique word order based on noun, verb, object, adjective, or part of a question. Marathi sign language follows the subject-object-verb (SOV) form, facilitating comprehension and adaptation to regional languages. This Marathi sign language gloss aims to become a medium for everyday communication among deaf individuals. This dataset reflects a careful curation process, simulating real-world scenarios. The original videos showcase a variety of gestures performed by a professional signer capturing a broad spectrum of sign language expressions. Incorporating Realism with green screen with controlled lighting conditions. All videos within this dataset adhere to pixels, ensuring uniformity for data presentation and facilitating streamlined pre-processing and model development stored in a format compatible with various machine and Deep learning frameworks, these videos seamlessly integrate into the research pipeline

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:

Hindi and English Bilingual Spontaneous Monologue smartphone speech dataset, collected from dialogues based on given topics, covering generic domain. Our dataset was collected from extensive and diversify speakers(302 people in total, ages 18 to 46), geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Format

16k Hz, 16 bit, wav, mono channel

Content category

Individuals naturally speaking, with no specific content limitations. Each speaker records 20 audios in each language (40 recordings per person), each recording lasting about 10-20 seconds

Recording condition

Quiet indoor environment, without echoes, background voices, obvious noises

Recording device

Android phone, iPhone

Speaker

Total 302 contributors,45% male and 55% female. 291contributors aged 18-37, 10 contributors aged 38-45, and 1 contributor aged 46-65

Country

India(IND)

Language

Hindi,English

Introduction

This Indian 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 Indian 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:

Introduction

This Indian 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 Indian 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 Indian 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 Indian 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:

The English 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 English 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

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

Voice Artist Selection

All voice artists are native English 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.

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.

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.

Introduction

Introducing the Indian English Scripted Monologue Speech Dataset for the Real Estate Domain, a dataset designed to support the development of English speech recognition and conversational AI technologies tailored for the real estate industry.

Speech Data

This dataset includes over 6,000 high-quality scripted prompt recordings in Indian English. The speech content reflects a wide range of real estate interactions to help build intelligent, domain-specific customer support systems and speech-enabled tools.

Topic and Scenario Coverage

This dataset captures a broad spectrum of use cases and conversational themes within the real estate sector, such as:

Contextual Depth

Each scripted prompt incorporates key elements to simulate realistic real estate conversations:

Transcription

To ensure precision in model training, each audio recording is paired with a verbatim text transcription:

Metadata

Each data sample is enriched with detailed metadata to enhance usability: