title: An algorithm to increase speech intelligibility for hearing-impaired listeners in novel segments of the same noise type.
abstract: Machine learning algorithms to segregate speech from background noise hold considerable promise for alleviating limitations associated with hearing impairment. One of the most important considerations for implementing these algorithms into devices such as hearing aids and cochlear implants involves their ability to generalize to conditions not employed during the training stage. A major challenge involves the generalization to novel noise segments. In the current study, sentences were segregated from multi-talker babble and from cafeteria noise using an algorithm that employs deep neural networks to estimate the ideal ratio mask. Importantly, the algorithm was trained on segments of noise and tested using entirely novel segments of the same nonstationary noise type. Substantial sentence-intelligibility benefit was observed for hearing-impaired listeners in both noise types, despite the use of unseen noise segments during the test stage. Interestingly, normal-hearing listeners displayed benefit in babble but not in cafeteria noise. This result highlights the importance of evaluating these algorithms not only in human subjects, but in members of the actual target population.
authors.affiliation: Department of Speech and Hearing Science, Center for Cognitive and Brain Sciences, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Eric W
authors.initials: EW
authors.lastname: Healy
authors.affiliation: Department of Speech and Hearing Science, Center for Cognitive and Brain Sciences, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Yoho
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Jitong
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: Yuxuan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, Center for Cognitive and Brain Sciences, The Ohio State University, Columbus, Ohio 43210, USA.
authors.forename: DeLiang
authors.initials: D
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D034381:Hearing Loss / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009622:Noise; D010470:Perceptual Masking / Q000502:physiology; D013061:Speech Acoustics; D013065:Speech Intelligibility / Q000502:physiology*; D013067:Speech Perception / Q000502:physiology