Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Communication Sciences and Disorders

Major Professor

Erol J. Ozmeral, Ph.D.

Committee Member

Nathan Higgins, Ph.D.

Committee Member

Victoria Sanchez, Au.D., Ph.D., CCC-A

Committee Member

David Eddins, Ph.D., CCC-A

Committee Member

Don Hayes, Ph.D.

Keywords

Environment Classification, acoustic scene analysis, Own Voice

Abstract

Hearing aids (HA) have improved over the past few decades with advanced signal processing, such as environment classification and background noise reduction, but are still limited by challenges related to when the user is speaking. These challenges include the potential interference a HA user’s voice has on device environment classification and the potential influence a HA has on own voice (OV) sound quality. However, the effects of OV on HA environment classification and signal processing are not well established, nor are the desired HA program settings regarding OV listening preferences. In this study, a combination of electroacoustic and behavioral methods were used to investigate how a HA user’s OV impacts HA environment classification, in addition to how HA processing influence user perception of OV sound quality. The central hypotheses are 1) a hearing aid classifies the speech of a hearing aid user differently than that of a partner resulting in shifts in the amount of signal processing features (e.g. noise reduction) applied to the output of the device; 2) the preferred frequency-gain shaping of hearing aid amplification for speech will be different for a hearing aid user’s own voice compared to that of a partner. The first aim (Chapter 2) uses recently developed acoustic manikins capable of simulating speech acoustics to investigate the interaction between a HA user’s OV, background noise, and conversational partner speech, on HA environment classification. The second aim (Chapter 3) is an analysis of OV effects using audio, video, and HA classification data obtained from experimental sessions investigating human communication in a naturalistic conversational setting. The third and final aim (Chapter 4) investigates aided OV sound quality by having individuals with hearing loss rate the sound quality of simulated HA user OV recordings in quiet and background noise using a Multiple Stimulus with Hidden Reference and Anchor (MUSHRA) task. The results of these aims have important implications for HA design, testing, and the clinical application of HA as medical devices.

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