Hypokinetic dysarthria is a common manifestation of Parkinson's disease (PD). Articulation characteristics can provide useful information to distinguish dysarthric speakers from healthy subjects and monitor the severity of disease and treatment effects. The aim of this study was to design an algorithm for automatic segmentation of consonants and vowels based upon a rapid steady /pa/-/ta/-/ka/ syllable repetition. All syllables were manually labeled at three positions including explosion (E), vowel (V), and occlusion (O). In addition, the representative measurement of voice onset time (VOT) was included as difference between V and E position. When compared to the manual labeled positions, the VOT is detected within the range 5ms to 20ms with a range of a success rate of 68.2 - 90.5%, 44.1 - 75.2%, and 57.2-83.5% for normal, dysarthric, and all speakers. In conclusion, this study shows that algorithm based on the spectral, Bayesian, and polynomial approaches, that can be used to accurately detect the positions of consonant and vowels in normal and dysarthria-related utterances.
Keywords: Parkinson’s disease, dysarthria, articulation, diadochokinetic
Autoři: Michal Novotný; Jan Rusz; Roman Čmejla Působiště autorů: Czech Technical University, Faculty of Electrical Engineering, Prague, Czech Rep. Vyšlo v časopise: Lékař a technika - Clinician and Technology No. 2, 2012, 42, 81-84 Kategorie: Conference YBERC 2012
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