Support Vector Machines with the priorities method for speaker independent phoneme recognition

Abstract

A speaker independent phoneme recognition system, based on Support Vector Machines (SVMs) method was improved by adding a priority scheme to forecast the three most likely phonemes. The system helps improve the obtained recognitions rate. For the phoneme recognition system, four multiclass SVMs methods, the All-at-once, One-against-all, One-against-one, and the Directed Acyclic Graph SVM (DAGSVM), were designed. The One-against-one method performed best, achieving an accuracy of 53.70%. This accuracy was further increased to 75.41%, when the second and third priorities were considered in the priorities method. All tests were carried out on the TIMIT database.