Abstract
This paper presents our solution for the DL Sprint 4.0, addressing the dual challenges of Bengali Long-Form Speech Recognition (Task 1) and Speaker Diarization (Task 2). Processing long-form, multi-speaker Bengali audio introduces significant hurdles in voice activity detection, overlapping speech, and context preservation. To solve the long-form transcription challenge, we implemented a robust audio chunking strategy utilizing whisper-timestamped, allowing us to feed precise, context-aware segments into our fine-tuned acoustic model for high-accuracy transcription. For the diarization task, we developed an integrated pipeline leveraging this http URL and WhisperX. A key contribution of our approach is the domain-specific fine-tuning of the Pyannote segmentation model on the competition dataset. This adaptation allowed the model to better capture the nuances of Bengali conversational dynamics and accurately resolve complex, overlapping speaker boundaries. Our methodology demonstrates that applying intelligent timestamped chunking to ASR and targeted segmentation fine-tuning to diarization significantly drives down Word Error Rate (WER) and Diarization Error Rate (DER), in low-resource settings.
Abstract (translated)
本文介绍了我们针对DL Sprint 4.0竞赛提出的解决方案，该方案旨在解决孟加拉语长语音识别（任务1）和说话人角色划分（任务2）的双重挑战。处理多说话人的孟加拉语文本带来了诸如语音活动检测、重叠言语以及上下文保持等方面的显著难题。 为了解决长文本转录的问题，我们实施了一种稳健的声音片段策略，利用了带有时间戳的whisper音频切分技术，使我们可以将精确且包含上下文信息的片段输入到经过微调的声学模型中以实现高精度转录。对于说话人角色划分的任务，我们开发了一个集成管道，结合使用上述方法和WhisperX工具。 我们的方法的一个关键贡献是，在竞赛数据集上对Pyannote分段模型进行了领域特定的微调。这种调整使模型能够更好地捕捉孟加拉语对话动态中的细微差别，并准确解析复杂的重叠说话人边界问题。我们的方法表明，将智能时间戳切片技术应用于自动语音识别（ASR）以及针对角色划分进行目标细分微调，在资源有限的情况下可以显著降低单词错误率（WER）和角色划分误差率（DER）。
URL
https://arxiv.org/abs/2603.04809
