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

arXiv: https://arxiv.org/abs/2304.04974 · HTML: https://mlsanity.com/paper/2304.04974/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 101 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 30 of 53)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Towards Robust Blind Face Restoration with Codebook Lookup Transformer](https://mlsanity.com/paper/2206.11253/) (2022)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [Self-supervised Learning with Random-projection Quantizer for Speech Recognition](https://mlsanity.com/paper/2202.01855/) (2022)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition](https://mlsanity.com/paper/2005.14327/) (2020)
- [PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network](https://mlsanity.com/paper/1911.04697/) (2019)
- [DPT-FSNet: Dual-path Transformer Based Full-band and Sub-band Fusion Network for Speech Enhancement](https://mlsanity.com/paper/2104.13002/) (2021)
- [Improving Transformer-based Speech Recognition Using Unsupervised Pre-training](https://mlsanity.com/paper/1910.09932/) (2019)
- [Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data](https://mlsanity.com/paper/2203.15321/) (2022)

## Built on by (most cited first, 10 of 10)

- [GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators](https://mlsanity.com/paper/2402.06894/) (2024)
- [Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models](https://mlsanity.com/paper/2405.10025/) (2024)
- [Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2305.09212/) (2023)
- [Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10563/) (2023)
- [R-Spin: Efficient Speaker and Noise-invariant Representation Learning with Acoustic Pieces](https://mlsanity.com/paper/2311.09117/) (2023)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10567/) (2023)
- [Speech Separation with Pretrained Frontend to Minimize Domain Mismatch](https://mlsanity.com/paper/2411.03085/) (2024)
- [Causal Self-supervised Pretrained Frontend with Predictive Code for Speech Separation](https://mlsanity.com/paper/2504.02302/) (2025)
- [Bootstrapping Audiovisual Speech Recognition in Zero-AV-Resource Scenarios](https://mlsanity.com/paper/2603.08249/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) has gained remarkable successes thanks to recent advances of deep learning, but it usually degrades significantly under real-world noisy conditions. 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. Based on that, 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. In this paper, we propose a self-supervised framework named Wav2code to implement a feature-level SE with reduced distortions for noise-robust ASR. First, in pre-training stage the clean speech representations from SSL model are sent to lookup a discrete codebook via nearest-neighbor feature matching, the resulted code sequence are then exploited to reconstruct the original clean representations, in order to store them in codebook as prior. Second, during finetuning we propose a Transformer-based code predictor to accurately predict clean codes by modeling the global dependency of input noisy representations, which enables discovery and restoration of high-quality clean representations with reduced distortions. Furthermore, we propose an interactive feature fusion network to combine original noisy and the restored clean representations to consider both fidelity and quality, resulting in more informative features for downstream ASR. Finally, experiments on both synthetic and real noisy datasets demonstrate that Wav2code can solve the speech distortion and improve ASR performance under various noisy conditions, resulting in stronger robustness.