# End-to-End Integration of Speech Recognition, Dereverberation, Beamforming, and Self-Supervised Learning Representation

arXiv: https://arxiv.org/abs/2210.10742 · HTML: https://mlsanity.com/paper/2210.10742/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2022.

## Built on by (most cited first, 6 of 6)

- [The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios](https://mlsanity.com/paper/2306.13734/) (2023)
- [A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning](https://mlsanity.com/paper/2305.13331/) (2023)
- [Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation](https://mlsanity.com/paper/2307.12231/) (2023)
- [RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios](https://mlsanity.com/paper/2311.00146/) (2023)
- [SpatialEmb: Extract and Encode Spatial Information for 1-Stage Multi-channel Multi-speaker ASR on Arbitrary Microphone Arrays](https://mlsanity.com/paper/2601.18037/) (2026)
- [Visual-Informed Speech Enhancement Using Attention-Based Beamforming](https://mlsanity.com/paper/2603.05270/) (2026)

## Abstract (arXiv)

Self-supervised learning representation (SSLR) has demonstrated its significant effectiveness in automatic speech recognition (ASR), mainly with clean speech. Recent work pointed out the strength of integrating SSLR with single-channel speech enhancement for ASR in noisy environments. This paper further advances this integration by dealing with multi-channel input. We propose a novel end-to-end architecture by integrating dereverberation, beamforming, SSLR, and ASR within a single neural network. Our system achieves the best performance reported in the literature on the CHiME-4 6-channel track with a word error rate (WER) of 1.77%. While the WavLM-based strong SSLR demonstrates promising results by itself, the end-to-end integration with the weighted power minimization distortionless response beamformer, which simultaneously performs dereverberation and denoising, improves WER significantly. Its effectiveness is also validated on the REVERB dataset.