Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data

• URL: http://arxiv.org/abs/2203.15321v1
• Date: Tue, 29 Mar 2022 08:06:01 GMT
• Title: Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data
• Authors: Chen Chen, Nana Hou, Yuchen Hu, Shashank Shirol, Eng Siong Chng
• Abstract summary: We propose a generative adversarial network to simulate noisy spectrum from the clean spectrum (Simu-GAN) We also propose a dual-path speech recognition system to improve the robustness of the system under noisy conditions.
• Score: 24.512424190830828
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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).

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