Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques

TL;DR

This paper evaluates the effectiveness of Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Dynamic Time Warping (DTW) for pattern matching in voice recognition systems, demonstrating their viability.

Contribution

It demonstrates the combined use of MFCC and DTW as an effective approach for voice recognition, highlighting their suitability for handling temporal variations in speech signals.

Findings

MFCC effectively extracts relevant voice features.

DTW improves pattern matching accuracy.

The approach is viable for real-time voice recognition systems.

Abstract

Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. Several methods such as Liner Predictive Predictive Coding (LPC), Hidden Markov Model (HMM), Artificial Neural Network (ANN) and etc are evaluated with a view to identify a straight forward and effective method for voice signal. The extraction and matching process is implemented right after the Pre Processing or filtering signal is performed. The non-parametric method for modelling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) are utilize as extraction techniques. The non linear sequence alignment known as Dynamic Time Warping (DTW) introduced by Sakoe Chiba has been used as features matching techniques. Since it's obvious that the voice signal tends to have different temporal rate, the alignment is important to produce the better performance.This paper present the viability of MFCC to extract features and DTW to compare the test patterns.