# Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study

arXiv: https://arxiv.org/abs/2203.16757 · HTML: https://mlsanity.com/paper/2203.16757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Data Augmenting Contrastive Learning of Speech Representations in the Time Domain](https://mlsanity.com/paper/2007.00991/) (2020)
- [AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario](https://mlsanity.com/paper/2104.03603/) (2021)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [Multichannel End-to-end Speech Recognition](https://mlsanity.com/paper/1703.04783/) (2017)
- [CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency](https://mlsanity.com/paper/2005.13326/) (2020)
- [End-to-End Far-Field Speech Recognition with Unified Dereverberation and Beamforming](https://mlsanity.com/paper/2005.10479/) (2020)
- [Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients](https://mlsanity.com/paper/2011.05649/) (2020)
- [Quaternion Neural Networks for Multi-channel Distant Speech Recognition](https://mlsanity.com/paper/2005.08566/) (2020)

## Built on by (most cited first, 1 of 1)

- [Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization](https://mlsanity.com/paper/2210.03459/) (2022)

## Abstract (arXiv)

Recently, the end-to-end training approach for multi-channel ASR has shown its effectiveness, which usually consists of a beamforming front-end and a recognition back-end. However, the end-to-end training becomes more difficult due to the integration of multiple modules, particularly considering that multi-channel speech data recorded in real environments are limited in size. This raises the demand to exploit the single-channel data for multi-channel end-to-end ASR. In this paper, we systematically compare the performance of three schemes to exploit external single-channel data for multi-channel end-to-end ASR, namely back-end pre-training, data scheduling, and data simulation, under different settings such as the sizes of the single-channel data and the choices of the front-end. Extensive experiments on CHiME-4 and AISHELL-4 datasets demonstrate that while all three methods improve the multi-channel end-to-end speech recognition performance, data simulation outperforms the other two, at the cost of longer training time. Data scheduling outperforms back-end pre-training marginally but nearly consistently, presumably because that in the pre-training stage, the back-end tends to overfit on the single-channel data, especially when the single-channel data size is small.