Telugu (India) Call Center Speech Dataset for Telecom

The audio dataset comprises call center conversations for the Telecom domain, featuring native Telugu speakers from India. It includes speech data, detailed metadata and accurate transcriptions.

About this Off-the-shelf Speech Dataset

Introduction

Welcome to the Telugu Call Center Speech Dataset for the Telecom domain designed to enhance the development of call center speech recognition models specifically for the Telecom industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Telecom domain, designed to build robust and accurate customer service speech technology.

•Participant Diversity:

•

Speakers: 60 expert native Telugu speakers from the FutureBeeAI Community.

•

Regions: Different regions of Andhra Pradesh and Telangana, ensuring a balanced representation of Telugu accents, dialects, and demographics.

•

Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

•Recording Details:

•

Conversation Nature: Unscripted and spontaneous conversations between call center agents and customers.

•

Call Duration: Average duration of 5 to 15 minutes per call.

•

Formats: WAV format with stereo channels, a bit depth of 16 bits, and a sample rate of 8 and 16 kHz.

•

Environment: Without background noise and without echo.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

•Inbound Calls:

•Phone Number Porting

•Network Connectivity Issues

•Billing and Payments

•Technical Support

•Service Activation

•International Roaming Enquiry

•Refunds and Billing Adjustments

•Emergency Service Access, and many more

•Outbound Calls:

•Welcome Calls / Onboarding Process

•Payment Reminders

•Customer Surveys

•Technical Updates

•Service Usage Reviews

•Network Compliant Status Call, and many more

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

•

Speaker-wise Segmentation: Time-coded segments for both agents and customers.

•

Non-Speech Labels: Tags and labels for non-speech elements.

•

Word Error Rate: Word error rate is less than 5% thanks to the dual layer of QA.

These ready-to-use transcriptions accelerate the development of the Telecom domain call center conversational AI and ASR models for the Telugu language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

•

Participant Metadata: Unique identifier, age, gender, country, state, district, accent and dialect.

•

Conversation Metadata: Domain, topic, call type, outcome/sentiment, bit depth, and sample rate.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Telugu call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Telecom domain. Potential use cases include:

•

Speech Recognition Models: Training and fine-tuning speech recognition models for Telugu.

•

Speech Analytics Models: Building speech analytics models to extract insights, identify patterns, and glean valuable information from customer conversation, enables data-driven decision-making and process optimization within the Telecom sector.

•

Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Telecom industries.

•

Sentiment Analysis: Analyzing customer sentiment and improving customer experience based on call center interactions.

•

Generative AI: Training generative AI models capable of generating human-like responses, summaries, or content tailored to the Telecom domain.

Secure and Ethical Collection

•Our proprietary data collection and transcription platform, “Yugo” was used throughout the process of this dataset creation.

•Throughout the data collection process, the data remained within our secure platform and did not leave our environment, ensuring data security and confidentiality.

•The data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.

•It does not include any personally identifiable information about any participant, which makes the dataset safe to use.

•The dataset does not contain any copyrighted content.

Updates and Customization

Understanding the importance of diverse environments for robust ASR models, our call center voice dataset is regularly updated with new audio data captured in various real-world conditions.

•Customization & Custom Collection Options:

•

Environmental Conditions: Custom collection in specific environmental conditions upon request.

•

Sample Rates: Customizable from 8kHz to 48kHz.

•

Transcription Customization: Tailored to specific guidelines and requirements.

License

This Telecom domain call center audio dataset is created by FutureBeeAI and is available for commercial use.

Use Cases

Call Center Conversational AI

Use of speech data for Automatic Speech Recognition

ASR

Chatbot

Language Modelling

TTS

Speech Analytics

Dataset Sample(s)

00:00

ATTRIBUTES

TRANSCRIPTION

Dataset Details

Language

Telugu

Language code

te-in

Country

India

Accents

Berad, Dasari ...moreDommara, Golari, Kamathi, Komtao, Konda-Reddi, Salewari, Vadaga, Srikakula, Vishakhapatnam, East Godaveri, Rayalseema, Nellore, Guntur, Vadari

Gender Distribution

M:60, F:40

Age Group

18-70