On the Use of Spectral Feature Fusions for Enhanced Performance of Malaysian English Accents Classification

Accent problem is a current issue that degrades the intelligibility and performance of speech recognition (ASR) systems. Despite English accents have been extensively researched in the United States, Britain, Australia, China, India, and Singapore, the study of Malaysian English (MalE) is still at infancy. There is till date, very limited evidence to corroborate how ethnically diverse accents in MalE of its three main ethnics can be identified from their speech signals. Most studies about MalE tackles issues from the view point of attitudinal studies and making use of human perceptual analysis. Instead, this paper presents experimental methods by means of acoustical analysis and machine learning techniques. In order to enhance the performance of accent classifier to classify the Malay, Chinese, and Indian accents this paper proposes fusion techniques of popularly known mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) with formants termed here as spectral feature fusions (SFFs). In these SFFs feature extractors, the main spectral features are fused with five usable formants and the extracted features are used to model K-nearest neighbors and artificial neural networks (ANN). Using independent test samples technique, gender-dependent accent classifiers were evaluated. Experimental results showed that the proposed SFFs surpassed the baseline features by 7.8 and 3.9 % increment of the classification rates for the LPC-formants and MFCC-formants fusions, respectively. The highest accuracies yielded for the fusion of MFCC and formants were 96.4 and 92.5 % on the male and female datasets. Speaking of LPC-formants fusion, the results were also promising, i.e., 92.6 and 88.8 % on the male and female datasets, respectively.