An Improved Feature Extraction Method for Malay Vowel Recognition based on Spectrum Delta

Malay speech recognition is becoming popular among Malaysian researchers. In Malay-sia, more local researchers are focusing on noise robust and accurate independent speaker speech recognition systems that use Malay language. The performance of speech recognition application under adverse noisy condition often becomes the topic of interest among speech recognition researchers in any languages. This paper presents a study of noise robust capa-bility of an improved vowel feature extraction method called Spectrum Delta (SpD). The fea-tures are extracted from both original data and noise-added data and classified using three classifiers; (i) Linear Discriminant Analysis (LDA), (ii) K-Nearest Neighbors (k-NN) and (iii) Multinomial Logistic Regression (MLR). Most of the dependent and independent speaker systems which use mostly multi-framed analysis, yielded accuracy between 89 % to 100 % for dependent speaker system and between 70 % to 94 % for an independent speaker. This study shows that SpD features obtained an accuracy of 92.42 % to 95.11 % using all the four classi-fiers on a single framed analysis which makes this result comparable to those analysed with multi-framed approach