Explainable Disentanglement on Discrete Speech Representations for Noise-Robust ASR

Shreyas Gopal; Ashutosh Anshul; Haoyang Li; Yue Heng Yeo; Hexin Liu; Eng Siong Chng

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arXiv · 2510.25150

Explainable Disentanglement on Discrete Speech Representations for Noise-Robust ASR

Shreyas Gopal·Ashutosh Anshul·Haoyang Li·Yue Heng Yeo·Hexin Liu·Eng Siong Chng

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Abstract

Discrete audio representations are gaining traction in speech modeling due to their interpretability and compatibility with large language models, but are not always optimized for noisy or real-world environments. Building on existing works that quantize Whisper embeddings for speech-to-unit modeling, we propose disentangling semantic speech content from background noise in the latent space. Our end-to-end model separates clean speech in the form of codebook tokens, while extracting interpretable noise vectors as quantization residue which are supervised via a lightweight classifier. We show that our approach improves alignment between clean/noisy speech and text, producing speech tokens that display a high degree of noiseinvariance, and improves ASR performance. Keeping Whisper frozen, we show an 82% reduction in error rate compared to Whisper, and 35% improvement over baseline methods on the VBDemand test set. Further analyses show that the learned token space generalizes well to both seen and unseen acoustic conditions.

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BibTeX RIS

Shreyas Gopal, Ashutosh Anshul, Haoyang Li, Yue Heng Yeo, Hexin Liu, Eng Siong Chng. 2025-10-29. Explainable Disentanglement on Discrete Speech Representations for Noise-Robust ASR. https://arxiv.org/abs/2510.25150

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