Development of Acoustical Feature Based Classifier Using Decision Fusion Technique for Malay Language Disfluencies Classification

Abstract

Speech disfluency such as filled pause (FP) is a hindrance in Automated Speech Recognition as it degrades the accuracy performance. Previous work of FP detection and classification have fused a number of acoustical features as fusion classification is known to improve classification results. This paper presents new decision fusion of two well-established acoustical features that are zero crossing rates (ZCR) and speech envelope (ENV) with eight popular acoustical features for classification of Malay language filled pause (FP) and elongation (ELO). Five hundred ELO and 500 FP are selected from a spontaneous speeches of a parliamentary session and Naïve Bayes classifier is used for the decision fusion classification. The proposed feature fusion produced better classification performance compared to single feature classification with the highest F-measure of 82% for both classes.