Robust Long-Form Bangla Speech Processing: Automatic Speech Recognition and Speaker Diarization
This addresses the problem of accurate speech processing for low-resource languages like Bengali, which has challenges such as dialectal variation and code-mixing, but the approach is incremental as it builds on existing models like Whisper and pyannote.audio.
The paper tackled robust long-form Bangla speech processing by developing an end-to-end system for automatic speech recognition (ASR) and speaker diarization, achieving a best private Word Error Rate of 0.37738 and Diarization Error Rate of 0.27671 in a Kaggle competition.
We describe our end-to-end system for Bengali long-form speech recognition (ASR) and speaker diarization submitted to the DL Sprint 4.0 competition on Kaggle. Bengali presents substantial challenges for both tasks: a large phoneme inventory, significant dialectal variation, frequent code-mixing with English, and a relative scarcity of large-scale labelled corpora. For ASR we achieve a best private Word Error Rate (WER) of 0.37738 and public WER of 0.36137, combining a BengaliAI fine-tuned Whisper medium model with Demucs source separation for vocal isolation, silence-boundary chunking, and carefully tuned generation hyperparameters. For speaker diarization we reach a best private Diarization Error Rate (DER) of 0.27671 and public DER of 0.20936 by replacing the default segmentation model inside the pyannote.audio pipeline with a Bengali-fine-tuned variant, pairing it with wespeaker-voxceleb-resnet34-LM embeddings and centroid-based agglomerative clustering. Our experiments demonstrate that domain-specific fine-tuning of the segmentation component, vocal source separation, and natural silence-aware chunking are the three most impactful design choices for low-resource Bengali speech processing.