HASA-net: A non-intrusive hearing-aid speech assessment network [article]

Hsin-Tien Chiang, Yi-Chiao Wu, Cheng Yu, Tomoki Toda, Hsin-Min Wang, Yih-Chun Hu, Yu Tsao
2021 arXiv   pre-print
Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. Recently, deep neural network (DNN) models have been applied to build non-intrusive speech assessment approaches and confirmed to provide promising performance. However, most DNN-based approaches are designed for normal-hearing listeners without considering hearing-loss factors. In this study, we propose a DNN-based hearing aid speech assessment network
more » ... , formed by a bidirectional long short-term memory (BLSTM) model, to predict speech quality and intelligibility scores simultaneously according to input speech signals and specified hearing-loss patterns. To the best of our knowledge, HASA-Net is the first work to incorporate quality and intelligibility assessments utilizing a unified DNN-based non-intrusive model for hearing aids. Experimental results show that the predicted speech quality and intelligibility scores of HASA-Net are highly correlated to two well-known intrusive hearing-aid evaluation metrics, hearing aid speech quality index (HASQI) and hearing aid speech perception index (HASPI), respectively.
arXiv:2111.05691v1 fatcat:igjek4ggqfcn5fm74dnznyiaau