Speech Perception of Hearing-impaired Listeners in Challenging Listening Environments and Personalization of Hearing Assistive Devices via Inverse Reinforcement Learning




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Listening to a speech in presence of a noise has always been a challenge specially for individuals with hearing impairment. There are many aspects that needs to be considered in designing and fitting of hearing assistive devices to provide users with a more preferred hearing experience or increase the perceived quality of speech and decrease the listening effort. This dissertation focuses on this topic in two major research thrust. In the first research thrust, the speech perception of hearing impaired listeners has been studied in challenging hearing environments. Behavioral and electrophysiological experiments have been designed to evaluate the effect of speech and noise levels on the perceived quality of speech and selective auditory attention in normal hearing and hearing impaired listeners. The perception of degraded speeches in normal hearing and hearing impaired listeners have been measured and the differences between the hearing patterns in these groups have been described. It has been shown that to achieve an optimal hearing experience, the listener’s hearing situation should be taken into account. In the second research thrust, the maximum likelihood inverse reinforcement learning approach has been followed to develop an algorithm to personalize the hearing aids fitting in an online manner. The results of the experiments conducted on subjects with hearing loss demonstrates the outperformance of the developed personalized setting over the standard prescriptive setting.



Engineering, Biomedical, Engineering, Electronics and Electrical