Noise-robust speech recognition systems require large amounts of training
data including noisy speech data and corresponding transcripts to achieve
state-of-the-art performances in face of various practical environments.
However, such plenty of in-domain data is not always available in the real-life
world. In this paper, we propose a generative adversarial network to simulate
noisy spectrum from the clean spectrum (Simu-GAN), where only 10 minutes of
unparalleled in-domain noisy speech data is required as labels. Furthermore, we
also propose a dual-path speech recognition system to improve the robustness of
the system under noisy conditions. Experimental results show that the proposed
speech recognition system achieves 7.3% absolute improvement with simulated
noisy data by Simu-GAN over the best baseline in terms of word error rate
(WER).

本文介绍了一种基于生成对抗网络（GAN）的噪声鲁棒性语音识别系统，只需使用 10 分钟的领域内无法匹配的嘈杂语音数据即可实现噪声谱的仿真。同时，我们还提出了一种双通道语音识别系统来提高在嘈杂环境下的稳健性能，实验证明所提出的系统通过 Simu-GAN 仿真噪声数据，比最佳基线在词错误率（WER）方面提高了 7.3%。