# Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition

arXiv: https://arxiv.org/abs/2203.14838 · HTML: https://mlsanity.com/paper/2203.14838/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling](https://mlsanity.com/paper/1903.04567/) (2019)
- [Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition](https://mlsanity.com/paper/2110.05267/) (2021)
- [Interactive Speech and Noise Modeling for Speech Enhancement](https://mlsanity.com/paper/2012.09408/) (2020)
- [Dual Application of Speech Enhancement for Automatic Speech Recognition](https://mlsanity.com/paper/2011.03840/) (2020)

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

- [Metric-oriented Speech Enhancement using Diffusion Probabilistic Model](https://mlsanity.com/paper/2302.11989/) (2023)
- [Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation](https://mlsanity.com/paper/2302.11131/) (2023)
- [Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition](https://mlsanity.com/paper/2302.11362/) (2023)
- [Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR](https://mlsanity.com/paper/2304.04974/) (2023)
- [Unsupervised Noise adaptation using Data Simulation](https://mlsanity.com/paper/2302.11981/) (2023)
- [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)
- [MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2306.10567/) (2023)
- [Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training](https://mlsanity.com/paper/2307.12498/) (2023)
- [Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends](https://mlsanity.com/paper/2608.30326/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems degrade significantly under noisy conditions. Recently, speech enhancement (SE) is introduced as front-end to reduce noise for ASR, but it also suppresses some important speech information, i.e., over-suppression. To alleviate this, we propose a dual-path style learning approach for end-to-end noise-robust speech recognition (DPSL-ASR). Specifically, we first introduce clean speech feature along with the fused feature from IFF-Net as dual-path inputs to recover the suppressed information. Then, we propose style learning to map the fused feature close to clean feature, in order to learn latent speech information from the latter, i.e., clean "speech style". Furthermore, we also minimize the distance of final ASR outputs in two paths to improve noise-robustness. Experiments show that the proposed approach achieves relative word error rate (WER) reductions of 10.6% and 8.6% over the best IFF-Net baseline, on RATS and CHiME-4 datasets respectively.