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arXiv · 2108.12105

Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement

Yuzi Yan·Wei-Qiang Zhang·Michael T. Johnson
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Abstract

As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing. In this paper, a new deep learning structure for speech enhancement is demonstrated. The model introduces a "full" attention mechanism to a bidirectional sequence-to-sequence method to make use of latent information after each focal frame. This is an extension of the previous attention-based RNN method. The proposed bidirectional attention-based architecture achieves better performance in terms of speech quality (PESQ), compared with OM-LSA, CNN-LSTM, T-GSA and the unidirectional attention-based LSTM baseline.

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BibTeXRIS

Yuzi Yan, Wei-Qiang Zhang, Michael T. Johnson. 2021-08-27. Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement. https://arxiv.org/abs/2108.12105

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