Khasi Speech Recognition using Hidden Markov Model with Different Spectral Features: A Comparison

Abstract

In this work, various feature extraction techniques are implemented for a speech data corpus in Khasi Language. This paper provides a comparative performance analysis of widely used spectral features like Mel-Frequency Cepstrum Coefficients (MFCC), Perceptual Linear Prediction (PLP), Linear Predictive Cepstrum Coefficient (LPCC), Linear Predictive Coefficient (LPC) and Linear Predictive Reflection Coefficients (LPREFC). For each features we have used coefficient dimensions of static, delta and acceleration evaluated for the recognition of words spoken in Khasi language, a language spoken in the Khasi and Jaintia hills districts of Meghalaya. A recorder based continuous speech database has been collected as per the standard procedure and data processing was done using wave surfer. From the analysis, it was observed that for sampling rate of 16 KHz and 8 KHz the word recognition accuracy of MFCC and PLP features are better than LPCC, LPC and LPREFC features under all circumstances.