Wav2code: Restoring Clean Speech Representations from Noisy Inputs for Robust Automatic Speech Recognition

Core Concepts

The proposed Wav2code framework leverages self-supervised learning and vector quantization to restore high-quality clean speech representations from noisy inputs, enabling more robust automatic speech recognition performance under various noisy conditions.

Abstract

The key highlights and insights of the content are: Automatic speech recognition (ASR) systems often degrade significantly under real-world noisy conditions. Recent works have explored two successful approaches to improve noise robustness: 1) joint speech enhancement (SE) and ASR systems, and 2) self-supervised learning (SSL). While SE can reduce noise, it may also cause speech distortion that is detrimental to downstream ASR. SSL can learn robust speech representations, but does not fully utilize the unlabeled clean speech data. The proposed Wav2code framework aims to address these limitations. It first pre-trains a codebook to store clean speech representations as prior, by performing nearest-neighbor matching and reconstruction from clean SSL features. In the finetuning stage, Wav2code employs a Transformer-based code predictor to accurately predict the clean codebook entries from noisy inputs, enabling restoration of high-quality clean representations with reduced distortions. Furthermore, an interactive feature fusion network is proposed to combine the restored clean representations and original noisy representations, in order to achieve both high fidelity and quality for downstream ASR. Experiments on both synthetic and real noisy datasets demonstrate that Wav2code outperforms state-of-the-art baselines, achieving stronger noise robustness for ASR.

Stats

"The power of neural networks has brought significant improvement to ASR performance as well as a simpler end-to-end training pipeline." "Despite the effectiveness of neural network-based ASR approaches, their performance usually degrades significantly under real-world noisy scenarios." "Experiments on both the synthetic noisy LibriSpeech data and the real noisy CHiME-4 data demonstrate that our proposed Wav2code achieves consistent ASR improvements under various noisy conditions, which shows stronger noise robustness."

Quotes

"Recent works introduce speech enhancement (SE) as front-end to improve speech quality, which is proved effective but may not be optimal for downstream ASR due to speech distortion problem." "To this end, latest works combine SE and currently popular self-supervised learning (SSL) to alleviate distortion and improve noise robustness. Despite the effectiveness, the speech distortion caused by conventional SE still cannot be cleared out."

Key Insights Distilled From

Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR

by Yuchen Hu,Ch... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2304.04974.pdf

Deeper Inquiries

How can the proposed Wav2code framework be extended to other speech-related tasks beyond ASR, such as speech synthesis or voice conversion

The Wav2code framework, which focuses on restoring clean speech representations for noise-robust ASR, can be extended to other speech-related tasks beyond ASR, such as speech synthesis or voice conversion, by adapting the core principles of the framework to suit the requirements of these tasks. Here are some ways in which the Wav2code framework can be extended: Speech Synthesis: In the context of speech synthesis, the Wav2code framework can be utilized to enhance the quality and fidelity of synthesized speech. By leveraging the codebook lookup and code prediction mechanisms to generate high-quality clean representations, the synthesized speech can benefit from reduced distortions and improved clarity. This can lead to more natural and intelligible speech output in speech synthesis systems. Voice Conversion: For voice conversion tasks, the Wav2code framework can be employed to transform the speech characteristics of one speaker to match those of another. By using the learned codebook and code prediction models, the framework can facilitate the conversion of speech features while preserving the essential qualities of the original speech. This can enable more accurate and effective voice conversion between different speakers. Speaker Verification: In speaker verification tasks, where the goal is to authenticate the identity of a speaker based on their voice, the Wav2code framework can aid in enhancing the robustness of speaker verification systems to noise and variations in speech signals. By restoring clean speech representations, the framework can help improve the accuracy and reliability of speaker verification models in challenging acoustic environments. Emotion Recognition: In the domain of emotion recognition from speech, the Wav2code framework can be applied to enhance the emotional content of speech signals. By restoring clean representations and reducing distortions caused by noise, the framework can improve the performance of emotion recognition models in accurately detecting and classifying different emotional states conveyed through speech. By adapting the principles of the Wav2code framework to these speech-related tasks, it is possible to enhance the performance and robustness of models across a range of applications beyond ASR.