May 13, 2024, 4:46 a.m. | Dena Mujtaba, Nihar R. Mahapatra, Megan Arney, J. Scott Yaruss, Hope Gerlach-Houck, Caryn Herring, Jia Bin

cs.CL updates on

arXiv:2405.06150v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) systems, increasingly prevalent in education, healthcare, employment, and mobile technology, face significant challenges in inclusivity, particularly for the 80 million-strong global community of people who stutter. These systems often fail to accurately interpret speech patterns deviating from typical fluency, leading to critical usability issues and misinterpretations. This study evaluates six leading ASRs, analyzing their performance on both a real-world dataset of speech samples from individuals who stutter and a synthetic dataset …

abstract accuracy arxiv asr automatic speech recognition biases challenges community education employment face global healthcare inclusivity lost mobile people recognition speech speech recognition systems technology transcription type

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