Statistical formant descriptors with linear predictive coefficients for accent classification

Accent is a special trait of human speech that can deliver some information about a speaker's background. At the same time it is one of the profound factors that affects the intelligibility and performance of speech recognition systems (ASRs) if not delicately handled. Normally accent recognizer in the preceding stage offers subsystem training or adaptation strategy to improve the ASRs. Formant analysis is one of the effective techniques used to extract accent information in speech. In this paper we propose a novel way of modifying formants using statistical descriptors and fusion with linear predictive coefficients (LPC). As a result, the deviation of scores from the means can be reduced and resulted in better accuracy rate. This work was based on database of accents in Malaysian English that are ethnically diverse in nature. Experimental results showed that the proposed fusion of LPC with statistically derived fmntRRS has achieved an increase of 7.61% in the accuracy rate over using LPC alone in the quest to classify three-accent problem.