A comparative study of different features for isolated spoken word recognition using HMM with reference to Assamese language

This paper describes the work done in implementation of speaker independent, isolated word recognizer for Assamese language. Linear predictive coding (LPC) analysis, LPC cepstral coefficients (LPCEPSTRA), linear mel-filter bank channel outputs and mel frequency cepstral coefficients (MFCC) are used to get the acoustical features. The hidden Markov model toolkit (HTK) using the Hidden Markov Model (HMM) has been used to build the different recognition models. The speech recognition model is trained for 10 Assamese words representing the digits from 0 (shounya) to 9 (no) in the Assamese language using fifteen speakers. Different models were created for each word which varied on the number of input feature values and the number of hidden states. The system obtained a maximum accuracy of 80 % for 39 MFCC features and a 7 state HMM model with 5 hidden states for a system with clean data and a maximum accuracy of 95 % for 26 LPCESPTRA features and a 7 state HMM model with 5 hidden states for a system with noisy data.