TY - GEN
T1 - A Model-Based Hearing Compensation Method Using a Self-Supervised Framework
AU - Niu, Yadong
AU - Li, Nan
AU - Wu, Xihong
AU - Chen, Jing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.
AB - Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.
KW - Hearing compensation
KW - self-supervised learning
KW - speech quality
UR - http://www.scopus.com/inward/record.url?scp=85177607364&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10095767
DO - 10.1109/ICASSP49357.2023.10095767
M3 - Conference contribution
AN - SCOPUS:85177607364
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -
