Abstract
Bengali remains a low-resource language in speech technology, especially for complex tasks like long-form transcription and speaker diarization. This paper presents a multistage approach developed for the "DL Sprint 4.0 - Bengali Long-Form Speech Recognition" and "DL Sprint 4.0 - Bengali Speaker Diarization" competitions on Kaggle, addressing the challenge of "who spoke when/what" in hour-long recordings. We implemented Whisper Medium fine-tuned on Bengali data (bengaliAI/tugstugi bengaliai-asr whisper-medium) for transcription and integrated pyannote/speaker-diarization-community-1 with our custom-trained segmentation model to handle diverse and noisy acoustic environments. Using a two-pass method with hyperparameter tuning, we achieved a DER of 0.27 on the private leaderboard and 0.19 on the public leaderboard. For transcription, chunking, background noise cleaning, and algorithmic post-processing yielded a WER of 0.38 on the private leaderboard. These results show that targeted tuning and strategic data utilization can significantly improve AI inclusivity for South Asian languages. All relevant code is available at: this https URL Index Terms: Bengali speech recognition, speaker diarization, Whisper, ASR, low-resource languages, pyannote, voice activity detection
Abstract (translated)
孟加拉语在语音技术领域仍然是资源匮乏的语言，尤其是在长篇转录和说话人划分等复杂任务方面。本文介绍了一种多阶段方法，该方法用于Kaggle上的“DL Sprint 4.0 - 孟加拉语长文语音识别”和“DL Sprint 4.0 - 孟加拉语说话人划分”竞赛，以解决小时级录音中“谁在什么时候说了什么”的挑战。我们使用了针对孟加拉语数据微调的Whisper Medium（由bengaliAI/tugstugi bengaliai-asr whisper-medium提供）进行转录，并结合pyannote/speaker-diarization-community-1和自定义训练的分割模型来处理各种嘈杂的声学环境。通过采用两步法并调整超参数，我们在私人排行榜上实现了0.27的DER（错误接受率），在公共排行榜上实现为0.19。对于转录，我们采用了切片、背景噪音清理和算法后处理技术，在私人排行榜上达到了0.38的WER（单词错误率）。这些结果显示了针对特定语言进行针对性调整和策略性数据利用可以显著提高AI对南亚语言的包容性。所有相关代码均可在此链接访问：this https URL 关键词包括：孟加拉语语音识别、说话人划分、Whisper、ASR（自动语音识别）、资源匮乏的语言、pyannote、声音活动检测。
URL
https://arxiv.org/abs/2603.03158
