Multi-resolution analysis of linear prediction coefficients using discrete wavelet transform for automatic accent recognition of diverse ethnics in Malaysian english

Abstract

Accent is a major cause of variability in speaker-independent automatic speech recognition (ASR) systems. Under certain circumstances, this behavioral factor introduces unsatisfactory performance of the systems. Thus, accent analyzer in the preceding stage of the ASR system becomes a promising solution. This paper proposes a multi-resolution approach which applies discrete wavelet transform (DWT) to conventional linear prediction coefficients (LPC) to optimize the extraction of accent from speech utterances in Malaysian English. This paper introduces a multi-numbered LPC (dyadic DWT-LPC) using a defined scale named as level dyadic division scale and an equal-numbered LPC (uniform DWT-LPC) approaches. Using the extracted features, accent models based on K-nearest neighbors were developed. Experimental results showed that the proposed multi-resolution dyadic DWT-LPC and uniform DWT-LPC features surpassed the conventional LPC by significant increases of classification rate of 12.7 and 17.0% respectively. The promising results of 93.4% and 88.5% were achieved using the proposed methods.