Effects of speech training on the accuracy of speech recognition for an individual with a speech impairment

Kotler, Ava-Lee; Thomas‐Stonell, Nancy

doi:10.1080/07434619712331277858

Cited by 36 publications

(16 citation statements)

References 6 publications

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“…Experiments with commercial ASR systems have shown levels of recognition accuracy up to 90% for some dysarthric speakers with high intelligibility after a certain number of tests, although speakers with lower intelligibility did not achieve comparable levels of recognition accuracy [11,[19][20][21][22]. Most of the speakers involved in these studies presented individual error patterns, and variability in recognition rates was observed between test sessions and when trying different ASR systems.…”

Section: Introductionmentioning

confidence: 99%

Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

Morales

Cox

2009

EURASIP J. Adv. Signal Process.

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Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.

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Section: Introductionmentioning

confidence: 99%

Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

Morales

Cox

2009

EURASIP J. Adv. Signal Process.

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“…Speech impairments also are common with all types of cerebral palsy (Kotler & Thomas-Stonell, 1997) and can impact writing. Speech is often affected due to motor coordination issues.…”

Section: Types Of Physical Disabilities That Affect Writingmentioning

confidence: 99%

“…This finding indicates that the quality of the software is correlated to the success with users with dysarthria. Kotler and Thomas-Stonell (1997) conducted a study with an individual with spastic cerebral and mild dysarthria. They found that the amount of speech training was correlated to the accuracy of speech recognition with people with dysarthria.…”

Section: Description Of Speech Recognition Software Speech Recognitimentioning

confidence: 99%

“…Speech recognition software, which provides hands-free access to the computer, was developed to facilitate the production of written work (Kotler & Thomas-Stonell, 1997). As the user speaks into a microphone, the words are transformed into text on the screen.…”

Section: Speech Recognition Softwarementioning

confidence: 99%

“…Because students with physical disabilities often have dysarthric speech, a further investigation explored the use of speech recognition software for this population (Kotler & Thomas-Stonell, 1997). Speech problems that limit the use of speech recognition include decreased intelligibility, phonemic limitations, slower speech rate, variable speech patterns, involuntary sounds, and inconsistency (Kotler & Thomas-Stonell;Rosen & Yampolsky, 2000). Additionally, environmental factors, such as fatigue and time of day need to be considered due to adverse affects on speech (Hux et.…”

Section: Description Of Speech Recognition Software Speech Recognitimentioning

confidence: 99%

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Using Speech Recognition Software to Increase Writing Fluency for Individuals with Physical Disabilities

Garrett

Heller

Fowler

et al. 2011

J Spec Educ Technol

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Students with physical disabilities often have difficulty with writing fluency, despite the use of various strategies, adaptations, and assistive technology (AT). One possible intervention is the use of speech recognition software, although there is little research on its impact on students with physical disabilities. This study used an alternating treatments design to compare the use of speech recognition software to word processing on first draft writing with students with physical disabilities. Areas examined were fluency, accuracy, type of word errors, recall of intended meaning, and length. The results of this study indicated that fluency and length were greater for all participants with speech recognition compared to word processing, but accuracy was lower using speech recognition.

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