@inbook{7bbce1cabc7c40d4a3e17003e59dd22a,
title = "Communication improves when human or computer listeners adapt to dysarthria",
abstract = "This chapter reviews methods that improve the ability of human and machine listeners to understand dysarthric speech. Traditionally, a speaker-oriented approach has been the dominant technique to improve the intelligibility of dysarthric speech. Recent work demonstrates the potential of listeners{\textquoteright} role in enhancing intelligibility. For human listeners, a training method called familiarization is evidenced to be effective, especially when the training is structured in a way to maximize perceptual learning. For machine listeners, the accuracy in understanding dysarthric speech can be improved by adaptive machine learning methods, with an initial model that already incorporates information about speakers{\textquoteright} characteristic speech patterns. Future direction to optimize the training results for human and machine listeners is discussed.",
author = "Heejin Kim and Mark Hasegawa-Johnson",
year = "2020",
month = aug,
day = "10",
doi = "10.1515/9781501513138-005",
language = "English (US)",
isbn = "9781501519628",
series = "Speech Technology and Text Mining in Medicine and Health Care",
publisher = "De Gruyter",
pages = "181--198",
editor = "Amy Neustein and Patil, {Hemant A.}",
booktitle = "Acoustic Analysis of Pathologies",
}