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Objective voice and speech analysis of persons with chronic hoarseness by prosodic analysis of speech samples
T. Haderlein, M. Döllinger, V. Matoušek, E. Nöth,
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články
- MeSH
- akustika řeči * MeSH
- akustika MeSH
- chrapot diagnóza patofyziologie MeSH
- chronická nemoc MeSH
- čtení MeSH
- dospělí MeSH
- kvalita hlasu * MeSH
- lidé středního věku MeSH
- lidé MeSH
- měření tvorby řeči metody MeSH
- mladiství MeSH
- mladý dospělý MeSH
- počítačové zpracování signálu * MeSH
- prediktivní hodnota testů MeSH
- regresní analýza MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované * MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- support vector machine MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Automatic voice assessment is often performed using sustained vowels. In contrast, speech analysis of read-out texts can be applied to voice and speech assessment. Automatic speech recognition and prosodic analysis were used to find regression formulae between automatic and perceptual assessment of four voice and four speech criteria. The regression was trained with 21 men and 62 women (average age 49.2 years) and tested with another set of 24 men and 49 women (48.3 years), all suffering from chronic hoarseness. They read the text 'Der Nordwind und die Sonne' ('The North Wind and the Sun'). Five voice and speech therapists evaluated the data on 5-point Likert scales. Ten prosodic and recognition accuracy measures (features) were identified which describe all the examined criteria. Inter-rater correlation within the expert group was between r = 0.63 for the criterion 'match of breath and sense units' and r = 0.87 for the overall voice quality. Human-machine correlation was between r = 0.40 for the match of breath and sense units and r = 0.82 for intelligibility. The perceptual ratings of different criteria were highly correlated with each other. Likewise, the feature sets modeling the criteria were very similar. The automatic method is suitable for assessing chronic hoarseness in general and for subgroups of functional and organic dysphonia. In its current version, it is almost as reliable as a randomly picked rater from a group of voice and speech therapists.
Citace poskytuje Crossref.org
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- $a Haderlein, Tino $u a Universitätsklinikum Erlangen, Phoniatrische und pädaudiologische Abteilung , Bohlenplatz 21, 91054 Erlangen , Germany. b Západočeská univerzita v Plzni, Katedra informatiky a výpočetní techniky , Univerzitní 8, 306 14 Plzeň , Czech Republic.
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- $a Objective voice and speech analysis of persons with chronic hoarseness by prosodic analysis of speech samples / $c T. Haderlein, M. Döllinger, V. Matoušek, E. Nöth,
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- $a Automatic voice assessment is often performed using sustained vowels. In contrast, speech analysis of read-out texts can be applied to voice and speech assessment. Automatic speech recognition and prosodic analysis were used to find regression formulae between automatic and perceptual assessment of four voice and four speech criteria. The regression was trained with 21 men and 62 women (average age 49.2 years) and tested with another set of 24 men and 49 women (48.3 years), all suffering from chronic hoarseness. They read the text 'Der Nordwind und die Sonne' ('The North Wind and the Sun'). Five voice and speech therapists evaluated the data on 5-point Likert scales. Ten prosodic and recognition accuracy measures (features) were identified which describe all the examined criteria. Inter-rater correlation within the expert group was between r = 0.63 for the criterion 'match of breath and sense units' and r = 0.87 for the overall voice quality. Human-machine correlation was between r = 0.40 for the match of breath and sense units and r = 0.82 for intelligibility. The perceptual ratings of different criteria were highly correlated with each other. Likewise, the feature sets modeling the criteria were very similar. The automatic method is suitable for assessing chronic hoarseness in general and for subgroups of functional and organic dysphonia. In its current version, it is almost as reliable as a randomly picked rater from a group of voice and speech therapists.
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- $a Döllinger, Michael $u a Universitätsklinikum Erlangen, Phoniatrische und pädaudiologische Abteilung , Bohlenplatz 21, 91054 Erlangen , Germany. c Louisiana State University, Communication Sciences and Disorders Department , 63 Hatcher Hall, Baton Rouge , LA 70803 , USA.
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- $a Matoušek, Václav $u b Západočeská univerzita v Plzni, Katedra informatiky a výpočetní techniky , Univerzitní 8, 306 14 Plzeň , Czech Republic.
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- $a Nöth, Elmar $u d Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung , Martensstraße 3, 91058 Erlangen , Germany. e King Abdulaziz University, Electrical & Computer Engineering Department, Faculty of Engineering , Jeddah 21589, Saudi Arabia.
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