March 14, 2024, 4:48 a.m. | Taekyung Ahn, Yeonjung Hong, Younggon Im, Do Hyung Kim, Dayoung Kang, Joo Won Jeong, Jae Won Kim, Min Jung Kim, Ah-ra Cho, Dae-Hyun Jang, Hosung Nam

cs.CL updates on

arXiv:2403.08187v1 Announce Type: new
Abstract: This study presents a model of automatic speech recognition (ASR) designed to diagnose pronunciation issues in children with speech sound disorders (SSDs) to replace manual transcriptions in clinical procedures. Since ASR models trained for general purposes primarily predict input speech into real words, employing a well-known high-performance ASR model for evaluating pronunciation in children with SSDs is impractical. We fine-tuned the wav2vec 2.0 XLS-R model to recognize speech as pronounced rather than as existing words. …

abstract arxiv asr automatic speech recognition children clinical diagnosis general recognition sound speech speech recognition study type

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