Assessment Of Dysarthric Speech Through Rhythm Metrics

• Published in: Journal of King Saud University. Computer and information sciences 2013, Vol.25 (1), p.43-49
• Main Authors: Dahmani, H, Selouani, S. A, O'shaughnessy, D, Chetouani, M, Doghmane, N
• Format: Article
• Language: English
• Subjects: Acoustical analysis; Artificial Intelligence; Cognitive science; Computer Science; Computer Vision and Pattern Recognition; Dysarthria; Dysarthric severity; Nemours database; Neuroscience; Pairwise variability index; Psychology; Rhythm; Robotics; Signal and Image Processing; Timing; اللسانيات الحاسوبية; علوم الحاسوب; قواعد البيانات
• ISSN: 1319-1578; 2213-1248; 1319-1578
• DOI: 10.1016/j.jksuci.2012.05.005

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, ΔV)). 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 (ΔDNV, %DV)).