Improving the performance of hearing aids in noisy environments based on deep learning technology

Ying Hui Lai, Wei Zhong Zheng, Shih Tsang Tang, Shih Hau Fang, Wen Huei Liao, Yu Tsao

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

The performance of a deep-learning-based speech enhancement (SE) technology for hearing aid users, called a deep denoising autoencoder (DDAE), was investigated. The hearing-aid speech perception index (HASPI) and the hearing- aid sound quality index (HASQI), which are two well-known evaluation metrics for speech intelligibility and quality, were used to evaluate the performance of the DDAE SE approach in two typical high-frequency hearing loss (HFHL) audiograms. Our experimental results show that the DDAE SE approach yields higher intelligibility and quality scores than two classical SE approaches. These results suggest that a deep-learning-based SE method could be used to improve speech intelligibility and quality for hearing aid users in noisy environments.

原文English
主出版物標題40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面404-408
頁數5
ISBN(電子)9781538636466
DOIs
出版狀態Published - 26 10月 2018
事件40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
持續時間: 18 7月 201821 7月 2018

出版系列

名字Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2018-July
ISSN(列印)1557-170X

Conference

Conference40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
國家/地區United States
城市Honolulu
期間18/07/1821/07/18

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