Automated rhythmic discrimination of dysarthria types
Rene L. Utianski, Visar Berisha, Julie Liss, Kaitlin L. Lansford · The Journal of the Acoustical Society of America · 2011
This study examines whether features of rhythm, extracted via an automated program, can match the success of hand-extracted metrics in discriminating among control and different types of dysarthric speech. Previously, Liss etal. [J. Speech, Language, and Hearing Res., 52(5), 1334–1352 (2009)] demonstrated that rhythm metrics could successfully separate individuals with perceptually distinct rhythm patterns, with the majority of classification functions more than 80% successful in classifying dysarthria type. However, the hand extracted rhythm measurements are labor intensive and require expertise in acoustic analysis to achieve valid and reliable measures. In this study, digitized speech was automatically segmented into vocalic and voiceless intervals using an autocorrelation-based algorithm, and, from these intervals, rhythm metrics were computed. Discriminant function analyses were performed and revealed the majority of functions were more than 90% successful in classification, matching, or exceeding the success of the hand- extracted measurements. Removing subjectivity, automating this procedure, and verifying its comparability with traditional methods improve the viability of rhythm metrics as a clinical tool in speech therapy intervention. Additionally, the ease of manipulation of the automated program will allow for development of metrics that capture severity and individual speaker differences that will inform models of speech perception.