Indian English Call Center Speech Dataset for Telecom

This Indian English speech dataset features real-world call center conversations from the Telecom domain. With detailed metadata and accurate transcriptions, it’s designed to power ASR systems, voice AI, and conversational agents.

About this Off-the-shelf Speech Dataset

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.

•Participant Diversity:

•

Speakers: 60 native Indian English speakers from our verified contributor pool.

•

Regions: Representing multiple provinces across India 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.

•

Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.

This metadata supports fine-grained analysis, dialect-specific tuning, and precise dataset segmentation.

Usage and Applications

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

•

Automatic Speech Recognition (ASR): Fine-tune English speech-to-text systems for telecom interactions.

•

Speech Analytics: Identify user pain points and improve telecom service delivery.

•

Voice Assistants & Chatbots: Build telecom virtual assistants for customer self-service.

•

Sentiment Analysis: Detect customer frustration or satisfaction in support calls.

•

Generative AI: Train telecom-specific summarization and response generation models.

Secure and Ethical Collection

•All data was collected using “Yugo,” FutureBeeAI’s proprietary platform under strict ethical and security standards.

•No personally identifiable information is included.

• The Dataset complies with global data privacy guidelines and is copyright-free.

Updates and Customization

We regularly expand this dataset with new telecom voice data and support full customization:

•Customization Options:

•

Acoustic Environment: Silent or noisy upon request.

•

Sample Rate: Customizable from 8kHz to 48kHz.

•

Transcription Format: Can follow your QA and formatting requirements.

License

This Telecom domain dataset is commercially licensed and ready for integration into English ASR, NLP, and voice AI solutions.

Use Cases

Call Center Conversational AI

Use of speech data for Automatic Speech Recognition

ASR

Chatbot

Language Modelling

TTS

Speech Analytics

Dataset Sample(s)

Dataset Details

Language

English

Language code

en-In

Country

India

Accents

Chandigarh, Chhattisgarh ...moreDelhi, Gujarat, Jammu and Kashmir, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Uttarakhand, West Bengal

Gender Distribution

M:60, F:40

Age Group

18-70 Years

File Details

Environment

Silent, Noisy

Bit Depth

16 bit

Format

wav

Sample rate

8khz & 16khz

Channel

Stereo (dual-channel, separated speakers)

Audio file duration

5-15 minutes

Read the License Terms

Browse FAQs

Similar to Call Center Conversation Speech Datasets

Vietnamese (Vietnam)

Vietnamese Telecom CC Speech Data

Telecom call center audio data in Vietnamese

30 Speech Hours

60 People

Call Center Conversational AI

ASR

Czech (Czech Republic)

Czech Telecom CC Speech Data

Telecom call center audio data in Czech

30 Speech Hours

60 People

Call Center Conversational AI

ASR

Malay (Malaysia)

Malay Telecom CC Speech Data

Telecom call center audio data in Malay

30 Speech Hours

60 People

Call Center Conversational AI

ASR

Arabic (Algeria)

Algerian Arabic Telecom CC Speech Data

Telecom call center audio data in Algerian Arabic.

30 Speech Hours

60 People

Call Center Conversational AI

ASR

View All

Philippine English Travel CC Speech Data

Travel call center audio data in Philippine English

30 Speech Hours

60 People

Call Center Conversational AI

ASR

English (Philippines)

Philippine English BFSI CC Speech Data

BFSI call center audio data in Philippine English

30 Speech Hours

60 People

Call Center Conversational AI

ASR

English (India)

Indian English Retail & E-com CC Speech Data

Retail & E-commerce call center audio data in Indian English.

30 Speech Hours

60 People

Call Center Conversational AI

ASR

English (UK)

British English Telecom CC Speech Data

Telecom call center audio data in British English.

30 Speech Hours

60 People

Call Center Conversational AI

ASR

View All

Need datasets for a specific AI/ML use case?
Don't worry, we've got you covered! 👍

Indian English Call Center Speech Dataset for Telecom

About this Off-the-shelf Speech Dataset

Introduction

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

•Participant Diversity:

•

Speakers: 60 native Indian English speakers from our verified contributor pool.

•

Regions: Representing multiple provinces across India 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.

•

Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.

This metadata supports fine-grained analysis, dialect-specific tuning, and precise dataset segmentation.

Usage and Applications

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

•

Automatic Speech Recognition (ASR): Fine-tune English speech-to-text systems for telecom interactions.

•

Speech Analytics: Identify user pain points and improve telecom service delivery.

•

Voice Assistants & Chatbots: Build telecom virtual assistants for customer self-service.

•

Sentiment Analysis: Detect customer frustration or satisfaction in support calls.

•

Generative AI: Train telecom-specific summarization and response generation models.

Secure and Ethical Collection

•All data was collected using “Yugo,” FutureBeeAI’s proprietary platform under strict ethical and security standards.

•No personally identifiable information is included.

• The Dataset complies with global data privacy guidelines and is copyright-free.

Updates and Customization

We regularly expand this dataset with new telecom voice data and support full customization:

•Customization Options:

•

Acoustic Environment: Silent or noisy upon request.

•

Sample Rate: Customizable from 8kHz to 48kHz.

•

Transcription Format: Can follow your QA and formatting requirements.

License

This Telecom domain dataset is commercially licensed and ready for integration into English ASR, NLP, and voice AI solutions.

Use Cases

Call Center Conversational AI

ASR

Chatbot

Language Modelling

TTS

Speech Analytics

Dataset Sample(s)

Dataset Details

Language

English

Language code

en-In

Country

India

Accents

Chandigarh, Chhattisgarh ...moreDelhi, Gujarat, Jammu and Kashmir, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Telangana, Uttar Pradesh, Uttarakhand, West Bengal

Gender Distribution

M:60, F:40

Age Group

18-70 Years