标题： 一种统一的去噪和适应框架用于自监督的孟加拉语方言自动语音识别 显示英文标题

标题： A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR

摘要： 自动语音识别（ASR）对于孟加拉语，世界上第五大语言，仍然是一个重大挑战，严重阻碍了其超过2.7亿使用者的技术可及性。 这一挑战由两个持续且相互交织的因素加剧：该语言的广泛方言多样性以及现实环境中声学噪声的普遍性。 虽然最先进的自监督学习（SSL）模型已经推动了低资源语言的ASR发展，但它们在预训练期间通常缺乏处理环境噪声的显式机制，也没有针对孟加拉语方言中复杂的语音和词汇变化的专门适应策略。 本文介绍了一种新的统一框架，旨在同时解决这两个挑战。 我们的方法基于WavLM模型，该模型通过掩码语音去噪目标进行独特预训练，使其本质上对声学失真具有鲁棒性。 我们提出了一种专门的多阶段微调策略，首先将模型适应到通用领域的标准孟加拉语，以建立强大的语言基础，然后通过有针对性的数据增强，将其专门用于噪声鲁棒的方言识别。 该框架在一个涵盖多种孟加拉语方言的全面基准上进行了严格评估，在从干净音频到低信噪比（SNR）水平的各种模拟噪声条件下进行测试。 实验结果表明，所提出的框架显著优于强大的基线，包括标准微调的wav2vec 2.0和大规模多语言Whisper模型。 这项工作为该任务设定了新的最先进水平，并为在全球范围内开发其他低资源、高变化语言的实际ASR系统提供了可扩展、有效的蓝图。 显示英文摘要

摘要： Automatic Speech Recognition (ASR) for Bengali, the world's fifth most spoken language, remains a significant challenge, critically hindering technological accessibility for its over 270 million speakers. This challenge is compounded by two persistent and intertwined factors: the language's vast dialectal diversity and the prevalence of acoustic noise in real-world environments. While state-of-the-art self-supervised learning (SSL) models have advanced ASR for low-resource languages, they often lack explicit mechanisms to handle environmental noise during pre-training or specialized adaptation strategies for the complex phonetic and lexical variations across Bengali dialects. This paper introduces a novel, unified framework designed to address these dual challenges simultaneously. Our approach is founded on the WavLM model, which is uniquely pre-trained with a masked speech denoising objective, making it inherently robust to acoustic distortions. We propose a specialized multi-stage fine-tuning strategy that first adapts the model to general-domain standard Bengali to establish a strong linguistic foundation and subsequently specializes it for noise-robust dialectal recognition through targeted data augmentation. The framework is rigorously evaluated on a comprehensive benchmark comprising multiple Bengali dialects under a wide range of simulated noisy conditions, from clean audio to low Signal-to-Noise Ratio (SNR) levels. Experimental results demonstrate that the proposed framework significantly outperforms strong baselines, including standard fine-tuned wav2vec 2.0 and the large-scale multilingual Whisper model. This work establishes a new state-of-the-art for this task and provides a scalable, effective blueprint for developing practical ASR systems for other low-resource, high-variation languages globally.