A Review of Various Techniques Related to Feature Extraction and Classification for Speech Signal Analysis

Abstract

AbstractCurrent technology development in the field of artificial intelligence and IoT has resulted in increased importance to research in speech processing. Researchers are emphasizing on speech processing and its applications due to increased acceptance of technology based on AI and IoT. Natural voice or speech signal available needs to be digitized for age in processing and feature extraction. Speech signal consist of scads of information categorized broadly as gender based, voice characteristics based, emotion based, speaker based etc. Recognizing the importance of feature extraction and classification for speech processing in various applications, significant research has been carried out for various methodologies related to diversified applications. This manuscript attempts to study and review the related research work in the field of feature extraction methodologies viz MFCC (Mel Frequency Cepstral Coefficient), LPC (Linear Predictive Coding), Wavelet, DWT (Discrete Wavelet Transform) and PLP (Perceptual Linear Predictive) etc. Researchers have also given importance to classifiers like SVM (Support Vector Machine), ANN (Artificial Neural Network), GMM (Gaussian Mixture model), HMM (Hidden Markov model) etc. The comparison of these classifiers has been presented in this review. The prime objective of this review paper is to observe the relationship between the variance of speech parameters, feature extraction methodologies and classifiers. The endeavor of this review is to establish the comparative observation which shall help the budding researchers for selection of feature extraction technique as well as classifier for various speech processing application considering specific advantages and disadvantages.KeywordsMFCCLDADWTWavelet feature extraction methodSVMGMMHMMANN classifier