Keyword-dependent monaural speech enhancement for open-vocabulary keyword spotting

被引：0

作者：

Liu, Zuozhen ^{[1
,2
]}

Wu, Chou ^{[1
]}

Li, Ta ^{[1
]}

Zhao, Qingwei ^{[1
]}

机构：

[1] Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing,100190, China

[2] University of Chinese Academy of Sciences, Beijing,100049, China

来源：

Shengxue Xuebao/Acta Acustica | 2023年 / 48卷 / 02期

关键词：

Deep learning - Encoding (symbols) - Signal encoding;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A monaural speech enhancement algorithm for open-vocabulary keyword spotting is proposed. The algorithm stores the keyword phoneme information in the text encoding matrix in advance, and adds a phoneme bias module based on the attention mechanism on the basis of the conventional speech enhancement model. This module uses the intermediate features of the speech enhancement model to obtain the phoneme information of the current frame from the text encoding matrix, and integrates it into the subsequent calculation of the speech enhancement model, so that the model can obtain better enhancement performance on the specified keywords. The experimental results in different noise environments show that the proposed method can more effectively suppress the noise of keyword part and better recover the speech details. Meanwhile, the proposed method achieves an 14.3% relative improvement in open-vocabulary keyword spotting compared to conventional speech enhancement method, and an 7.6% relative improvement compared to other text-dependent speech enhancement method. © 2023 Science Press. All rights reserved.

引用

页码：415 / 424