Gujarati Call Center Speech Dataset for Telecom

This Gujarati 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 Gujarati-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.

The dataset contains 30 hours of dual-channel call center recordings between native Gujarati 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.

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.

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.

Rich metadata is available for each participant and conversation:

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

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

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

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