Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention

• URL: http://arxiv.org/abs/2002.05873v1
• Date: Fri, 14 Feb 2020 05:05:36 GMT
• Title: Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
• Authors: Yuma Koizumi, Kohei Yatabe, Marc Delcroix, Yoshiki Masuyama, Daiki Takeuchi
• Abstract summary: This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features. We extract a speaker representation used for adaptation directly from the test utterance.
• Score: 70.82604384963679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance. Conventional studies of deep neural network (DNN)--based speech enhancement mainly focus on building a speaker independent model. Meanwhile, in speech applications including speech recognition and synthesis, it is known that model adaptation to the target speaker improves the accuracy. Our research question is whether a DNN for speech enhancement can be adopted to unknown speakers without any auxiliary guidance signal in test-phase. To achieve this, we adopt multi-task learning of speech enhancement and speaker identification, and use the output of the final hidden layer of speaker identification branch as an auxiliary feature. In addition, we use multi-head self-attention for capturing long-term dependencies in the speech and noise. Experimental results on a public dataset show that our strategy achieves the state-of-the-art performance and also outperform conventional methods in terms of subjective quality.

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