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pro vyhledávání: '"Tobin, Jimmy"'
Autor:
Jiang, Pan-Pan, Tobin, Jimmy, Tomanek, Katrin, MacDonald, Robert L., Seaver, Katie, Cave, Richard, Ladewig, Marilyn, Heywood, Rus, Green, Jordan R.
Project Euphonia, a Google initiative, is dedicated to improving automatic speech recognition (ASR) of disordered speech. A central objective of the project is to create a large, high-quality, and diverse speech corpus. This report describes the proj
Externí odkaz:
http://arxiv.org/abs/2409.09190
Autor:
Venugopalan, Subhashini, Tobin, Jimmy, Yang, Samuel J., Seaver, Katie, Cave, Richard J. N., Jiang, Pan-Pan, Zeghidour, Neil, Heywood, Rus, Green, Jordan, Brenner, Michael P.
We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a five-point
Externí odkaz:
http://arxiv.org/abs/2303.07533
Autor:
Tobin, Jimmy1 jtobin@google.com, Nelson, Phillip1, MacDonald, Bob1, Heywood, Rus1, Cave, Richard2, Seaver, Katie3, Desjardins, Antoine3, Pan-Pan Jiang1, Green, Jordan R.3,4
Publikováno v:
Journal of Speech, Language & Hearing Research. Nov2024, Vol. 67 Issue 11, p4176-4185. 10p.
Autor:
Tobin, Jimmy, Li, Qisheng, Venugopalan, Subhashini, Seaver, Katie, Cave, Richard, Tomanek, Katrin
Word Error Rate (WER) is the primary metric used to assess automatic speech recognition (ASR) model quality. It has been shown that ASR models tend to have much higher WER on speakers with speech impairments than typical English speakers. It is hard
Externí odkaz:
http://arxiv.org/abs/2209.10591
Autor:
Tobin, Jimmy, Tomanek, Katrin
This study investigates the performance of personalized automatic speech recognition (ASR) for recognizing disordered speech using small amounts of per-speaker adaptation data. We trained personalized models for 195 individuals with different types a
Externí odkaz:
http://arxiv.org/abs/2110.04612
Autor:
Venugopalan, Subhashini, Shor, Joel, Plakal, Manoj, Tobin, Jimmy, Tomanek, Katrin, Green, Jordan R., Brenner, Michael P.
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. Classification approaches can also help identify hard-to-recognize speech samples to teach ASR systems about t
Externí odkaz:
http://arxiv.org/abs/2107.03985