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arXiv · 2601.17679

BanglaRobustNet: A Hybrid Denoising-Attention Architecture for Robust Bangla Speech Recognition

Md Sazzadul Islam Ridoy·Mubaswira Ibnat Zidney·Sumi Akter·Md. Aminur Rahman
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Abstract

Bangla, one of the most widely spoken languages, remains underrepresented in state-of-the-art automatic speech recognition (ASR) research, particularly under noisy and speaker-diverse conditions. This paper presents BanglaRobustNet, a hybrid denoising-attention framework built on Wav2Vec-BERT, designed to address these challenges. The architecture integrates a diffusion-based denoising module to suppress environmental noise while preserving Bangla-specific phonetic cues, and a contextual cross-attention module that conditions recognition on speaker embeddings for robustness across gender, age, and dialects. Trained end-to-end with a composite objective combining CTC loss, phonetic consistency, and speaker alignment, BanglaRobustNet achieves substantial reductions in word error rate (WER) and character error rate (CER) compared to Wav2Vec-BERT and Whisper baselines. Evaluations on Mozilla Common Voice Bangla and augmented noisy speech confirm the effectiveness of our approach, establishing BanglaRobustNet as a robust ASR system tailored to low-resource, noise-prone linguistic settings.

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Md Sazzadul Islam Ridoy, Mubaswira Ibnat Zidney, Sumi Akter, Md. Aminur Rahman. 2026-01-25. BanglaRobustNet: A Hybrid Denoising-Attention Architecture for Robust Bangla Speech Recognition. https://arxiv.org/abs/2601.17679

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