Techniques for Feature Extraction In Speech Recognition System : A Comparative Study

TL;DR

This paper compares various feature extraction techniques for speech recognition, emphasizing the importance of selecting appropriate features to improve system performance and discussing their respective strengths and weaknesses.

Contribution

It provides a comparative analysis of common feature extraction methods in speech recognition, highlighting their significance and differences.

Findings

Different feature extraction methods have unique strengths and weaknesses.

The choice of features impacts speech recognition accuracy.

The paper reviews the importance of feature selection in speech systems.

Abstract

The time domain waveform of a speech signal carries all of the auditory information. From the phonological point of view, it little can be said on the basis of the waveform itself. However, past research in mathematics, acoustics, and speech technology have provided many methods for converting data that can be considered as information if interpreted correctly. In order to find some statistically relevant information from incoming data, it is important to have mechanisms for reducing the information of each segment in the audio signal into a relatively small number of parameters, or features. These features should describe each segment in such a characteristic way that other similar segments can be grouped together by comparing their features. There are enormous interesting and exceptional ways to describe the speech signal in terms of parameters. Though, they all have their strengths and weaknesses, we have presented some of the most used methods with their importance.