Automatic detection of speech disorder in dysarthria using extended speech feature extraction and neural networks classification

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Abstract

This paper presents an automatic detection of Dysarthria, a motor speech disorder, using extended speech features called Centroid Formants. Centroid Formants are the weighted averages of the formants extracted from a speech signal. This involves extraction of the first four formants of a speech signal and averaging their weighted values. The weights are determined by the peak energies of the bands of frequency resonance, formants. The resulting weighted averages are called the Centroid Formants. In our proposed methodology, these centroid formants are used to automatically detect Dysarthric speech using neural network classification technique. The experimental results recorded after testing this algorithm are presented. The experimental data consists of 200 speech samples from 10 Dysarthric Speakers and 200 speech samples from 10 age-matched healthy speakers. The experimental results show a high performance using neural networks classification. A possible future research related to this work is the use of these extended features in speaker identification and recognition of disordered speech.
Original languageEnglish
Number of pages6
Publication statusPublished - 4 Dec 2017
EventThe 3rd International Conference on Intelligent Signal Processing - Savoy Place, London, United Kingdom
Duration: 4 Dec 20175 Dec 2017
http://events.theiet.org/isp/

Conference

ConferenceThe 3rd International Conference on Intelligent Signal Processing
Abbreviated titleISP 2017
CountryUnited Kingdom
CityLondon
Period4/12/175/12/17
Internet address

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Keywords

  • dysarthria
  • speech disorder
  • neural networks
  • centroid formants

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