Comparison of Three Auditory Frequency Scales in Feature Extraction on Myanmar Digits Recognition

With the rapidly growth of digital computers, there has been an increasing demand to communicate with machines in efficient spoken manner. Speech recognition is the process of translating fromspoken words into readable text. To get the robust and reliable transcription text from recognizer,proper feature extraction methods are needed. This paper is concerned to an approach of features extraction on spoken Myanmar digits recognition. In this study, the recognition performances of Fast Fourier Transform (FFT), Mel Frequency Cepstrum Coefficients (MFCC), Linear Predictive Coding (LPC)and Linear Prediction Cepstral Coefficients (LPCC) methods will be compared. Even though the frequency spacing with Mel scale is extensively used in Automatic Speech Recognition (ASR), this paper demonstrates another scale of auditory frequency spectrum namely, Bark and Equivalent Rectangular Bandwidth (ERB) scales. The results have achieved the better performance than the Mel scale. The k-Nearest Neighbor (KNN) is employed as the classifier and ten digits of Myanmar language from twelve speakers are collected. According to these experiments, the results show the best recognition rates of 88.6% with the used of feature extraction based on ERB scale band pass filter.