Abstract
Despite being one of the most widely spoken languages globally, Bangla remains a low-resource language in the field of Natural Language Processing (NLP). Mainstream Automatic Speech Recognition (ASR) and Speaker Diarization systems for Bangla struggles when processing longform audio exceeding 3060 seconds. This paper presents a robust framework specifically engineered for extended Bangla content by leveraging preexisting models enhanced with novel optimization pipelines for the DL Sprint 4.0 contest. Our approach utilizes Voice Activity Detection (VAD) optimization and Connectionist Temporal Classification (CTC) segmentation via forced word alignment to maintain temporal accuracy and transcription integrity over long durations. Additionally, we employed several finetuning techniques and preprocessed the data using augmentation techniques and noise removal. By bridging the performance gap in complex, multi-speaker environments, this work provides a scalable solution for real-world, longform Bangla speech applications.
Abstract (translated)
尽管孟加拉语是全球使用最广泛的语言之一，但在自然语言处理（NLP）领域中，它仍然是一种资源匮乏的语言。主流的自动语音识别（ASR）和说话人聚类系统在处理超过3060秒的长音频时会遇到困难。本文提出了一种专门针对扩展孟加拉语内容设计的强大框架，并通过创新优化管道增强了现有的模型，以适应DL Sprint 4.0竞赛的要求。 我们的方法利用了语音活动检测（VAD）优化和连接时间分类（CTC）分段技术，通过强制词对齐来保持长时间内的时序准确性和转录完整性。此外，我们还采用了多种微调技术和数据预处理措施，包括使用增强技术和噪声消除来提高模型性能。 通过解决复杂多说话人环境中的性能差距问题，本研究为实际长音频孟加拉语应用提供了一种可扩展的解决方案。
URL
https://arxiv.org/abs/2602.22935
