Efficient Speech Recognition System Based on an Improved MFCC Features Using LWT

Abstract

Creating a system that can hear and respond accurately like a human is one of the most critical issues in human-computer interaction. This inspired the creation of the automatic speech recognition system, which uses efficient feature extraction and selection techniques to distinguish between different classes of speech signals. In order to improve the ASR (automatic speech recognition), the authors present a new feature extraction method in this study which is based on modified MFCC (mel frequency cepstral coefficients) using lifting wavelet transform LWT (lifting wavelet transform). The effectiveness of the proposed approach is verified using the datasets of the ATSSEE Research Unit “Analysis and Processing of Electrical and Energy Signals and Systems.” The experimental investigations have been carried out to demonstrate the practical viability of the proposed approach. Numerical and experimental studies concluded that the proposed approach is capable of detecting and localizing multiple under varying environmental conditions with noise-contaminated measurements.