BanglaRobustNet: A Hybrid Denoising-Attention Architecture for Robust Bangla Speech Recognition
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
BanglaRobustNet represents a substantial advancement in automatic speech recognition for the Bangla language, introducing innovative techniques to enhance robustness in challenging acoustic conditions. The combination of phonetic-aware denoising and speaker-conditioned attention mechanisms is particularly noteworthy, addressing critical gaps in existing ASR systems for low-resource languages.
Methodology
The methodology presented in BanglaRobustNet is innovative, combining a diffusion-based denoising module with a contextual cross-attention mechanism tailored for Bangla ASR. The design principles emphasize phonetic preservation and speaker adaptivity, which are critical for improving ASR performance in low-resource languages. The end-to-end training approach with a composite objective function is well-justified, addressing the unique challenges posed by Bangla phonetics and dialectal variations. However, while the architecture is robust, the paper could benefit from a more detailed discussion on the specific implementation of the denoising and attention mechanisms.
Results & evaluation
The experimental evaluation is thorough, utilizing diverse datasets that reflect real-world conditions, including both clean and noisy environments. The reported reductions in WER and CER compared to baseline models are significant, demonstrating the effectiveness of the proposed architecture. The use of statistical significance testing adds rigor to the results. However, the paper lacks a comprehensive ablation study that could provide deeper insights into the contributions of individual components of the architecture.
Reproducibility
The paper provides a reasonable level of detail regarding the training infrastructure, hyperparameters, and evaluation protocols, which supports reproducibility. However, the absence of a publicly available code repository or demo limits the ability for other researchers to replicate the results independently. Including a link to a GitHub repository or similar would enhance reproducibility.
Limitations
One limitation is the reliance on a relatively small dataset for training, which may affect the generalizability of the model to other dialects or noise conditions not represented in the training data. Additionally, the paper does not address potential biases in the training data or how they might impact the model's performance across different demographics.
Broader impact
The development of BanglaRobustNet has significant implications for improving access to technology for Bangla speakers, a demographic that has been underserved in the field of ASR. By enhancing speech recognition capabilities in noisy environments and across diverse speakers, this work could facilitate better communication tools and services for millions of users. Furthermore, the approach could inspire similar advancements in ASR for other low-resource languages. BanglaRobustNet represents a substantial advancement in automatic speech recognition for the Bangla language, introducing innovative techniques to enhance robustness in challenging acoustic conditions. The combination of phonetic-aware denoising and speaker-conditioned attention mechanisms is particularly noteworthy, addressing critical gaps in existing ASR systems for low-resource languages.
Author of this paper? Send a correction.