@inproceedings{82aebee297814cc49389e552f98218ef,
title = "Improving the performance of hearing aids in noisy environments based on deep learning technology",
abstract = "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.",
author = "Lai, {Ying Hui} and Zheng, {Wei Zhong} and Tang, {Shih Tsang} and Fang, {Shih Hau} and Liao, {Wen Huei} and Yu Tsao",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 ; Conference date: 18-07-2018 Through 21-07-2018",
year = "2018",
month = oct,
day = "26",
doi = "10.1109/EMBC.2018.8512277",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "404--408",
booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",
address = "美國",
}
