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An Investigation Into Various Approaches For Bengali Long-Form Speech Transcription and Bengali Speaker Diarization

arXiv – CS AI|Epshita Jahan, Khandoker Md Tanjinul Islam, Pritom Biswas, Tafsir Al Nafin|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers developed a multistage AI approach for Bengali speech transcription and speaker diarization, achieving significant improvements in processing long-form audio recordings. The system used fine-tuned Whisper models and custom segmentation techniques to address the low-resource nature of Bengali in speech technology applications.

Key Takeaways

→Fine-tuned Whisper Medium model achieved 0.38 WER for Bengali speech transcription on private leaderboard.
→Custom speaker diarization system achieved 0.27 DER using pyannote integration with specialized segmentation models.
→Two-pass methodology with hyperparameter tuning proved effective for handling noisy acoustic environments.
→Research demonstrates potential for improving AI inclusivity for South Asian low-resource languages.
→Complete implementation code made publicly available for community development and research.

#bengali #speech-recognition #whisper #speaker-diarization #low-resource-languages #ai-inclusivity #asr #pyannote #multilingual-ai

Read Original →via arXiv – CS AI

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