Speech Signal Analysis and Pattern Recognition in Diagnosis of Dysarthria

Speech Signal Analysis and Pattern Recognition in Diagnosis of Dysarthria

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...

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Bibliographic Details
Published in:Annals of the Indian Academy of Neurology Vol. 20; no. 4; pp. 352 - 357
Main Authors: Thoppil, Minu George, Kumar, C Santhosh, Kumar, Anand, Amose, John
Format: Journal Article
Language:English
Published: India Medknow Publications & Media Pvt. Ltd 01-10-2017
Medknow Publications & Media Pvt Ltd
Wolters Kluwer Medknow Publications
Subjects:
Acoustics
Ataxic
Computer programs
Data processing
dysarthria
extrapyramidal
F0 jitter
formant range
Frequency
Nervous system
Original
Parkinson's disease
Pattern recognition
pitch break
Population
Population studies
Software
spastic
Spasticity
Speech
Speech disorders
F0 jitter
spastic
extrapyramidal
formant range
pitch break
Ataxic
dysarthria
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Summary: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. (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. 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. 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. 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.
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ISSN:0972-2327
1998-3549
DOI:10.4103/aian.AIAN_130_17