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arXiv · 2203.15321

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

Chen Chen·Nana Hou·Yuchen Hu·Shashank Shirol·Eng Siong Chng
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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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Chen Chen, Nana Hou, Yuchen Hu, Shashank Shirol, Eng Siong Chng. 2022-03-29. Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data. https://arxiv.org/abs/2203.15321

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