# Multi-Channel Automatic Speech Recognition Using Deep Complex Unet

arXiv: https://arxiv.org/abs/2011.09081 · HTML: https://mlsanity.com/paper/2011.09081/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting](https://mlsanity.com/paper/2305.12331/) (2023)
- [Multi-channel Speech Enhancement with 2-D Convolutional Time-frequency Domain Features and a Pre-trained Acoustic Model](https://mlsanity.com/paper/2107.11222/) (2021)

## Abstract (arXiv)

The front-end module in multi-channel automatic speech recognition (ASR) systems mainly use microphone array techniques to produce enhanced signals in noisy conditions with reverberation and echos. Recently, neural network (NN) based front-end has shown promising improvement over the conventional signal processing methods. In this paper, we propose to adopt the architecture of deep complex Unet (DCUnet) - a powerful complex-valued Unet-structured speech enhancement model - as the front-end of the multi-channel acoustic model, and integrate them in a multi-task learning (MTL) framework along with cascaded framework for comparison. Meanwhile, we investigate the proposed methods with several training strategies to improve the recognition accuracy on the 1000-hours real-world XiaoMi smart speaker data with echos. Experiments show that our proposed DCUnet-MTL method brings about 12.2% relative character error rate (CER) reduction compared with the traditional approach with array processing plus single-channel acoustic model. It also achieves superior performance than the recently proposed neural beamforming method.