Objective voice and speech analysis of persons with chronic hoarseness by prosodic analysis of speech samples - Haderlein, Tino; Döllinger, Michael; Matoušek, Václav; Nöth, Elmar (Logopedics, phoniatrics, vocology, 2016)
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.
Keywords: Regression Analysis; dospělí; Support Vector Machine; Assessment; Signal Processing, Computer-Assisted; Female; akustika; měření tvorby řeči; počítačové zpracování signálu; Adult; ženské pohlaví; mladý dospělý; Predictive Value of Tests; Adolescent; Reproducibility of Results; prediktivní hodnota testů; lidé; voice disorders; automatic speech recognition; regresní analýza; Aged, 80 and over; Speech Acoustics; Pattern Recognition, Automated; technology; rozpoznávání automatizované; reprodukovatelnost výsledků; Male; Acoustics; Aged; Speech Production Measurement; Reading; Young Adult; chronická nemoc; akustika řeči; lidé středního věku; support vector machine; senioři nad 80 let; senioři; mužské pohlaví; Voice Quality; Hoarseness; čtení; Chronic Disease; kvalita hlasu; Middle Aged; mladiství; chrapot; prosodic analysis; Humans