June 11, 2024, 4:47 a.m. | Huma Ameer, Seemab Latif, Rabia Latif

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05784v1 Announce Type: cross
Abstract: The automated classification of stuttered speech has significant implications for timely assessments providing assistance to speech language pathologists. Despite notable advancements in the field, the cases in which multiple disfluencies occur in speech require attention. We have taken a progressive approach to fill this gap by classifying multi-stuttered speech more efficiently. The problem has been addressed by firstly curating a dataset of multi-stuttered disfluencies from SEP-28k audio clips. Secondly, employing Whisper, a state-of-the-art speech recognition …

abstract arxiv assessment attention automated cases classification cs.lg cs.sd eess.as encoder language multiple speech type whisper

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