Feature extraction and analysis of MISING speech vowels
Rizwan Rehman, Gopal Chandra HAZARIKA, Devid Kardong · 2016
Speech analysis forms the first layer in the process of automatic speech recognition. All speech recognition system primarily performs pattern recognition and therefore they perform well when inputs features are provided with certain properties. The Mel-Scale cepstral coefficient and LP coefficient transformed into cepstral coefficient are the best techniques for performing the automatic speech recognition. They are preferred because of their robustness with respect to noise. This study has been done to extract the Mel-Frequency Cepstral Coefficient (MFCC) and Linear Frequency Cepstral Coefficient (LFCC) features for the vowels in the MISING language and to analyze the parameters obtained.