Bone-Conducted Speech Enhancement Using Hierarchical Extreme Learning Machine

Tassadaq Hussain, Yu Tsao, Sabato Marco Siniscalchi, Jia Ching Wang, Hsin Min Wang, Wen Hung Liao

研究成果: 書貢獻/報告類型篇章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Deep learning-based approaches have demonstrated promising performance for speech enhancement (SE) tasks. However, these approaches generally require large quantities of training data and computational resources for model training. An alternate hierarchical extreme learning machine (HELM) model has been previously reported to perform SE and has demonstrated satisfactory results with a limited amount of training data. In this study, we investigate application of the HELM model to improve the quality and intelligibility of bone-conducted speech. Our experimental results show that the proposed HELM-based bone-conducted SE framework can effectively enhance the original bone-conducted speech and outperform a deep denoising autoencoder-based bone-conducted SE system in terms of speech quality and intelligibility with improved recognition accuracy when a limited quantity of training data is available.

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主出版物標題Lecture Notes in Electrical Engineering
發行者Springer Science and Business Media Deutschland GmbH
頁面153-162
頁數10
DOIs
出版狀態已出版 - 2021

出版系列

名字Lecture Notes in Electrical Engineering
714
ISSN(列印)1876-1100
ISSN(電子)1876-1119

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