SVM and HMM Modeling Techniques for Speech Recognition Using LPCC and MFCC Features

Abstract

Speech Recognition approach intends to recognize the text from the speech utterance which can be more helpful to the people with hearing disabled. Support Vector Machine (SVM) and Hidden Markov Model (HMM) are widely used techniques for speech recognition system. Acoustic features namely Linear Predictive Coding (LPC), Linear Prediction Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are extracted. Modeling techniques such as SVM and HMM were used to model each individual word thus owing to 620 models which are trained to the system. Each isolated word segment from the test sentence is matched against these models for finding the semantic representation of the test input speech. The performance of the system is evaluated for the words related to computer domain and the system shows an accuracy of 91.46% for SVM 98.92% for HMM. From the exhaustive analysis, it is evident that HMM performs better than other modeling techniques such as SVM.