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Dysarthria

About: Dysarthria is a research topic. Over the lifetime, 2402 publications have been published within this topic receiving 56554 citations.


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Journal ArticleDOI
TL;DR: Although older and younger adults had equivalent performance identifying words produced by talkers with dysarthria, older adults appear to utilize more cognitive support to identify those words.
Abstract: Purpose Previous research has demonstrated equivocal findings related to the effect of listener age on intelligibility ratings of dysarthric speech. The aim of the present study was to investigate ...

13 citations

Proceedings ArticleDOI
30 Mar 2015
TL;DR: A text-to-speech synthesis system that generates speech in a speaker's voice, but without the errors he makes would be desirable, and a system, where the dysarthric speech is first recognized by an HMM-based speech recognition system and a sentence-level network is used to ensure 100% recognition accuracy.
Abstract: Dysarthria is a manifestation of an inability to control and coordinate on one or more articulatory subsystems, which results in poorly articulated, slurred, and unintelligible speech. In order to enable a dysarthric speaker to communicate more efficiently with others, a text-to-speech synthesis system that generates speech in his voice, but without the errors he makes would be desirable. In this regard, the current work proposes a system, where the dysarthric speech is first recognized by an HMM-based speech recognition system. A sentence-level network is used to ensure 100% recognition accuracy. The recognized text is then synthesized by a speech synthesis system adapted to the dysarthric speaker's voice. This system replaces the sound units wrongly uttered by the dysarthric speaker, thereby improving intelligibility. The rate of synthesized speech is quite low for speakers with moderate and severe dysarthria. Therefore, the speech rate is modified using time-domain pitch synchronous overlap add (TD-PSOLA) technique. Degradation mean opinion score (DMOS) is used to prove that wrongly uttered sound units are replaced by correct sound units and that the synthetic speech is made more intelligible with the speaker's identity.

13 citations

Journal ArticleDOI
TL;DR: It is suggested that acoustic measures correlate with speech naturalness, but in dysarthric speech they depend on the speaker due to the within-speaker variation in speech impairment.
Abstract: This study investigated the acoustic basis of across-utterance, within-speaker variation in speech naturalness for four speakers with dysarthria secondary to Parkinson's disease (PD). Speakers read sentences and produced spontaneous speech. Acoustic measures of fundamental frequency, phrase-final syllable lengthening, intensity and speech rate were obtained. A group of listeners judged speech naturalness using a nine-point Likert scale. Relationships between judgements of speech naturalness and acoustic measures were determined for individual speakers with PD. Relationships among acoustic measures also were quantified. Despite variability between speakers, measures of mean F0, intensity range, articulation rate, average syllable duration, duration of final syllables, vocalic nucleus length of final unstressed syllables and pitch accent of final syllables emerged as possible acoustic variables contributing to within-speaker variations in speech naturalness. Results suggest that acoustic measures correlate with speech naturalness, but in dysarthric speech they depend on the speaker due to the within-speaker variation in speech impairment.

13 citations

Journal ArticleDOI
TL;DR: Results show the relevance of rhythm metrics to distinguish healthy speech from dysarthrias and to discriminate the levels of dysarthria severity.
Abstract: This paper reports the results of acoustic investigation based on rhythmic classifications of speech from duration measurements carried out to distinguish dysarthric speech from healthy speech. The Nemours database of American dysarthric speakers is used throughout experiments conducted for this study. The speakers are eleven young adult males with dysarthria caused by cerebral palsy (CP) or head trauma (HT) and one non-dysarthric adult male. Eight different sentences for each speaker were segmented manually to vocalic and intervocalic segmentation (176 sentences). Seventy-four different sentences for each speaker were automatically segmented to voiced and non-voiced intervals (1628 sentences). A two-parameters classification related to rhythm metrics was used to determine the most relevant measures investigated through bi-dimensional representations. Results show the relevance of rhythm metrics to distinguish healthy speech from dysarthrias and to discriminate the levels of dysarthria severity. The majority of parameters was more than 54% successful in classifying speech into its appropriate group (90% for the dysarthric patient classification in the feature space (%V, @DV)). The results were not significant for voiced and unvoiced intervals relatively to the vocalic and intervocalic intervals (the highest recognition rates were: 62.98 and 90.30% for dysarthric patient and healthy control classification respectively in the feature space (@DDNV, %DV)).

13 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023229
2022415
2021164
2020138
2019125
201888