An Efficient Use of SVM and QDA Algorithms on EPG Signals
G. Aravindh, M. Arunkumar · 2020
The children diagnosed with hearing disabilities will lack the ability to recognize a word or sentence when it is orated. The Computer aided language learning system will remain as a boon for the children ith hearing impairment. The solution for this challenge is articulatory speech learning that has been performed by using Electropalatography(EPG) methodology, which is usually a task based language learning. The dataset combines speech and EPG of English vowels and consonants. Different statistical classifiers are investigated to recognize the vowel of electropalatography signals. The support vector machine, quadratic discriminate analysis algorithms and Linear discriminate analysis are used to classify the vowels and consonents. SVM and QDA Algorithms have been implemented to analyze the articulatory synthesis and speech synthesis, which will together decide the intonation contour of the speech utterances and tongue gestures.