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Analysis of Feature Extraction Methods for Distinguishing the Speech of Cleft Palate Patients
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  • Journal title : Journal of KIISE
  • Volume 42, Issue 11,  2015, pp.1372-1379
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.11.1372
 Title & Authors
Analysis of Feature Extraction Methods for Distinguishing the Speech of Cleft Palate Patients
Kim, Sung Min; Kim, Wooil; Kwon, Tack-Kyun; Sung, Myung-Whun; Sung, Mee Young;
This paper presents an analysis of feature extraction methods used for distinguishing the speech of patients with cleft palates and people with normal palates. This research is a basic study on the development of a software system for automatic recognition and restoration of speech disorders, in pursuit of improving the welfare of speech disabled persons. Monosyllable voice data for experiments were collected for three groups: normal speech, cleft palate speech, and simulated clef palate speech. The data consists of 14 basic Korean consonants, 5 complex consonants, and 7 vowels. Feature extractions are performed using three well-known methods: LPC, MFCC, and PLP. The pattern recognition process is executed using the acoustic model GMM. From our experiments, we concluded that the MFCC method is generally the most effective way to identify speech distortions. These results may contribute to the automatic detection and correction of the distorted speech of cleft palate patients, along with the development of an identification tool for levels of speech distortion.
Distorted speech of patients with cleft palates;Sound recognition;Feature extraction;LPCC;MFCC;PLP;
 Cited by
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