Vowel Acoustics in Dysarthria: Speech Disorder Diagnosis and Classification

Abstract

Purpose The purpose of this study was to determine the extent to which vowel metrics are capable of distinguishing healthy from dysarthric speech and among different forms of dysarthria. Method A variety of vowel metrics were derived from spectral and temporal measurements of vowel tokens embedded in phrases produced by 45 speakers with dysarthria and 12 speakers with no history of neurological disease. Via means testing and discriminant function analysis (DFA), the acoustic metrics were used to (a) detect the presence of dysarthria and (b) classify the dysarthria subtype. Results Significant differences between dysarthric and healthy control speakers were revealed for all vowel metrics. However, the results of the DFA demonstrated some metrics (particularly metrics that capture vowel distinctiveness) to be more sensitive and specific predictors of dysarthria. Only the vowel metrics that captured slope of the second formant (F2) demonstrated between-group differences across the dysarthrias. However, when subjected to DFA, these metrics proved unreliable classifiers of dysarthria subtype. Conclusion The results of these analyses suggest that some vowel metrics may be useful clinically for the detection of dysarthria but may not be reliable indicators of dysarthria subtype using the current dysarthria classification scheme.

Clear Speech Variants: An Acoustic Study in Parkinson's Disease

“…Similar to Lam et al (2012), the greatest VSAs were produced in the overenunciate condition, followed by the hearing impaired and clear conditions. Although researchers have suggested that other vowel metrics such as vowel articulation index (VAI) and formant centralization ratio (FCR) are shown to be more sensitive to articulatory impairment in dysarthria (Sapir, Ramig, Spielman, & Fox, 2010;Skodda et al, 2011b), recent work by Lansford and Liss (2014) showed that VSA (80% accuracy) outperformed FCR (70% accuracy) in classifying speakers with dysarthria, despite the fact that the two metrics were highly correlated. Moreover, it has been suggested that speech production measures used to evaluate therapeutic effects should be able to index functional speech abilities (i.e., intelligibility; Weismer, Yunusova, & Bunton, 2012).…”

“…Acoustic variables likely explaining these intelligibility variations are suggested in a variety of studies. For example, segmental metrics such as static and dynamic vowel measures, consonant spectral moments, and consonant distinctiveness measures have been reported as predictors of intelligibility (Amano-Kusumoto, Holsom, Kain, & Aronoff, 2014;Fogerty, 2013;Fogerty & Humes, 2010;Kay, 2012;Kim et al, 2011;Lansford & Liss, 2014;Maniwa, Jongman, & Wade, 2008;Owren & Cardillo, 2006;Tjaden & Wilding, 2004;Turner et al, 1995;Weismer, Martin, Kent, & Kent, 1992). In addition, suprasegmental measures of F0 and global timing measures have shown to be strong predictors of intelligibility (Bradlow, Torretta, & Pisoni, 1996;Bunton, Kent, Kent, & Duffy, 2001;Kim et al, 2011;Laures & Weismer, 1999).…”

Listeners' attitudes toward accented talkers uniquely predicts accented speech perception

“…Talkers produced three recordings of the 80 semantically anomalous phrases from Lansford and Liss (2014) and three recordings of the Caterpillar Passage (Patel et al, 2013). The passage and phrases were recorded to disk using Adobe Audition at 44100 Hz at 16 bit accuracy via a Shure SM10A head-mounted microphone.…”

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

“…Dysarthria is characterized by monotony of pitch, reduced loudness, an irregular rate of speech and, imprecise consonants and changes in voice quality [36]. Speech-language pathologists do the evaluation, diagnosis and treat communication disorders.…”