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http://dx.doi.org/10.5909/JBE.2017.22.2.253

Machine Learning based Speech Disorder Detection System  

Jung, Junyoung (School of Electrical Engineering, Soongsil Univ.)
Kim, Gibak (School of Electrical Engineering, Soongsil Univ.)
Publication Information
Journal of Broadcast Engineering / v.22, no.2, 2017 , pp. 253-256 More about this Journal
Abstract
This paper deals with the implementation of speech disorder detection system based on machine learning classification. Problems with speech are a common early symptom of a stroke or other brain injuries. Therefore, detection of speech disorder may lead to correction and fast medical treatment of strokes or cerebrovascular accidents. The speech disorder system can be implemented by extracting features from the input speech and classifying the features using machine learning algorithms. Ten machine learning algorithms with various scaling methods were used to discriminate speech disorder from normal speech. The detection system was evaluated by the TORGO database which contains dysarthric speech collected from speakers with either cerebral palsy or amyotrophic lateral sclerosis.
Keywords
Speech disorder; Machine learning;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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