Automating Speech Audiometry in Quiet and in Noise Using a Deep Neural Network.

ABSTRACT: In addition to pure-tone audiometry tests and electrophysiological tests, a comprehensive hearing evaluation includes assessing a subject's ability to understand speech in quiet and in noise. In fact, speech audiometry tests are commonly used in clinical practice; however, they are time-consuming as they require manual scoring by a hearing professional. To address this issue, we developed an automated speech recognition (ASR) system for scoring subject responses at the phonetic level. The ASR was built using a deep neural network and trained with pre-recorded French speech materials: Lafon's cochlear lists and Dodelé logatoms. Next, we tested the performance and reliability of the ASR in clinical settings with both normal-hearing and hearing-impaired listeners. Our findings indicate that t

SUBMITTER: Jean H 

PROVIDER: S-EPMC11851792 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature