Bengali (Bangladesh) Call Center Speech Dataset for BFSI

This Bengali 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 Bengali-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.

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

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.

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.

Rich metadata is available for each participant and conversation:

This granularity supports BFSI-specific analytics, sentiment modeling, and dialect-aware ASR training.

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

We regularly expand this dataset with fresh call recordings and offer tailored options:

This BFSI domain dataset is commercially licensed and ready for integration into your Bengali ASR, NLP, or conversational AI pipeline.