A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909763463282688 |
|---|---|
| author | Biswas, Swadhin Imran Sheikh, Tuhin |
| author_facet | Biswas, Swadhin Imran Sheikh, Tuhin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR Biswas, Swadhin Imran Sheikh, Tuhin Sound Audio and Speech Processing 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. |
| title | A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.00988 |