Reconstructed State Space Model for Recognition of Consonant - Vowel (Cv) Utterances Using Support Vector Machines

Abstract

This paper presents a study on the use of Support Vector Machines (SVMs) in classifying Malayalam Consonant-Vowel (CV) speech unit by comparing it to two other classification algorithms namely Artificial Neural Network (ANN) and k-Nearest Neighbourhood (k-NN). We extend SVM to combine many two class classifiers into multiclass classifier using Decision Directed Acyclic Graph (DDAG) algorithm. A feature extraction technique using Reconstructed State Space(RSS) based State Space Point Distribution (SSPD) parameters are studied. We obtain an average recognition accuracy of 90% using SSPD for SVM based Malayalam CV speech unit database in speaker independent environments. The result shows that the efficiency of the proposed technique is capable for increasing speaker independent consonant speech recognition accuracy and can be effectively used for developing a complete speech recognition system for Malayalam language.