# Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator

arXiv: https://arxiv.org/abs/2203.13919 · HTML: https://mlsanity.com/paper/2203.13919/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [Far-Field Automatic Speech Recognition](https://mlsanity.com/paper/2009.09395/) (2020)
- [Multichannel End-to-end Speech Recognition](https://mlsanity.com/paper/1703.04783/) (2017)
- [Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition](https://mlsanity.com/paper/1903.05299/) (2019)
- [Coherent-to-Diffuse Power Ratio Estimation for Dereverberation](https://mlsanity.com/paper/1502.03784/) (2015)
- [Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition](https://mlsanity.com/paper/2109.04783/) (2021)
- [Deep Convolutional Neural Network-based Inverse Filtering Approach for Speech De-reverberation](https://mlsanity.com/paper/2010.07895/) (2020)

## Abstract (arXiv)

We present a novel multi-channel front-end based on channel shortening with theWeighted Prediction Error (WPE) method followed by a fixed MVDR beamformer used in combination with a recently proposed self-attention-based channel combination (SACC) scheme, for tackling the distant ASR problem. We show that the proposed system used as part of a ContextNet based end-to-end (E2E) ASR system outperforms leading ASR systems as demonstrated by a 21.6% reduction in relative WER on a multi-channel LibriSpeech playback dataset. We also show how dereverberation prior to beamforming is beneficial and compare the WPE method with a modified neural channel shortening approach. An analysis of the non-intrusive estimate of the signal C50 confirms that the 8 channel WPE method provides significant dereverberation of the signals (13.6 dB improvement). We also show how the weights of the SACC system allow the extraction of accurate spatial information which can be beneficial for other speech processing applications like diarization.