Spectral Features Analysis for Hindi Speech Recognition System

Abstract

Automatic speech recognition refers to recognizing the speech utterances and converting them to text through machines. For this purpose, the features forms an extremely important part. The richness of features will predict the performance of the overall system. So, this paper deals with the various speech features that can used for Hindi speech that has been tested for many other languages. In this work, MFCC, PLP, EFCC and LPC have been tested against Hindi Speech Corpus using HMM toolkit HTK 3.4.1. These features have been evaluated using common environment. The main objective of this paper is to summarize and compare the traditional and newer feature extraction methodology in automatic speech recognition system. This work favours EFCC features over other features. EFCC have shown a significant improvement in noisy environment in automatic speech recognition system.