Publication Type:

Journal Article

Source:

Annals of Indian Academy of Neurology, Medknow Publications, Volume 20, Number 4, p.352-357 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032929719&doi=10.4103%2faian.AIAN_130_17&partnerID=40&md5=d14029dd428f6116d0d98620afcf7d95

Keywords:

acoustic analysis, adult, article, ataxia, clinical article, comparative study, controlled study, cross-sectional study, disease severity, dysarthria, extrapyramidal syndrome, female, Fourier transformation, human, male, middle aged, observational study, Parkinson disease, Pattern recognition, Pitch, pseudobulbar palsy, sensitivity and specificity, spasticity, Speech analysis, waveform

Abstract:

Background: Dysarthria refers to a group of disorders resulting from disturbances in muscular control over the speech mechanism due to damage of central or peripheral nervous system. There is wide subjective variability in assessment of dysarthria between different clinicians. In our study, we tried to identify a pattern among types of dysarthria by acoustic analysis and to prevent intersubject variability. Objectives: (1) Pattern recognition among types of dysarthria with software tool and to compare with normal subjects. (2) To assess the severity of dysarthria with software tool. Materials and Methods: Speech of seventy subjects were recorded, both normal subjects and the dysarthric patients who attended the outpatient department/admitted in AIMS. Speech waveforms were analyzed using Praat and MATHLAB toolkit. The pitch contour, formant variation, and speech duration of the extracted graphs were analyzed. Results: Study population included 25 normal subjects and 45 dysarthric patients. Dysarthric subjects included 24 patients with extrapyramidal dysarthria, 14 cases of spastic dysarthria, and 7 cases of ataxic dysarthria. Analysis of pitch of the study population showed a specific pattern in each type. F0 jitter was found in spastic dysarthria, pitch break with ataxic dysarthria, and pitch monotonicity with extrapyramidal dysarthria. By pattern recognition, we identified 19 cases in which one or more recognized patterns coexisted. There was a significant correlation between the severity of dysarthria and formant range. Conclusions: Specific patterns were identified for types of dysarthria so that this software tool will help clinicians to identify the types of dysarthria in a better way and could prevent intersubject variability. We also assessed the severity of dysarthria by formant range. Mixed dysarthria can be more common than clinically expected.

Notes:

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Cite this Research Publication

M. Thoppil, Kumar, C., Kumar, A., and Amose, J., “Speech signal analysis and pattern recognition in diagnosis of dysarthria”, Annals of Indian Academy of Neurology, vol. 20, pp. 352-357, 2017.

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