Abstract
Although Automatic Speech Recognition (ASR) in Bengali has seen significant progress, processing long-duration audio and performing robust speaker diarization remain critical research gaps. To address the severe scarcity of joint ASR and diarization resources for this language, we introduce Lipi-Ghor-882, a comprehensive 882-hour multi-speaker Bengali dataset. In this paper, detailing our submission to the DL Sprint 4.0 competition, we systematically evaluate various architectures and approaches for long-form Bengali speech. For ASR, we demonstrate that raw data scaling is ineffective; instead, targeted fine-tuning utilizing perfectly aligned annotations paired with synthetic acoustic degradation (noise and reverberation) emerges as the singular most effective approach. Conversely, for speaker diarization, we observed that global open-source state-of-the-art models (such as Diarizen) performed surprisingly poorly on this complex dataset. Extensive model retraining yielded negligible improvements; instead, strategic, heuristic post-processing of baseline model outputs proved to be the primary driver for increasing accuracy. Ultimately, this work outlines a highly optimized dual pipeline achieving a $\sim$0.019 Real-Time Factor (RTF), establishing a practical, empirically backed benchmark for low-resource, long-form speech processing.
Abstract (translated)
尽管孟加拉语的自动语音识别（ASR）取得了显著进展，但处理长时间音频和进行稳健的说话人定位仍然是重要的研究空白。为了解决这种语言在联合ASR和说话人定位资源上的严重不足，我们引入了Lipi-Ghor-882，这是一个全面的882小时多说话者孟加拉语数据集。在这篇论文中，详细介绍了我们在DL Sprint 4.0竞赛中的提交内容，系统地评估了各种长格式孟加拉语音架构和方法。 对于ASR，我们证明了单纯的数据扩充效果不佳；相反，采用完美对齐的注释，并结合合成声学退化（如噪音和回响）进行目标细化是唯一最有效的策略。相比之下，在说话人定位方面，全球开源最先进的模型（如Diarizen），在处理这个复杂数据集时表现得出乎意料地差。大量的重新训练并未带来显著改进；反而，基于基线模型输出的战略性、启发式的后处理成为提高准确性的主要驱动力。 最终，这项工作概述了一种高度优化的双管道方法，达到了约0.019的真实时间因子（RTF），为低资源长时间语音处理建立了一个实用且经验支持的基准。
URL
https://arxiv.org/abs/2602.23070
