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

Introduction

The quickest, most natural, and most popular way for people to communicate is through speech. A speech signal is seen as a complex signal that not only transmits a message but also contains details about the speaker's personality, including gender, age, language, dialect, and emotional state. Speaking to a machine is one of the techniques to improve and speed up human-computer contact, which has developed as a result of technological advancement. As a result, during the past few decades, academics have investigated many ways to improve the effectiveness of spoken communication through systems like speaker and voice recognition. To develop a speech recognition system that can hear and reply naturally like a human is one of the industry's top. This has given rise to the crucial and difficult field of Automatic Speech Recognition (ASR) in recent years. The accuracy, speed, and intimacy of daily interactions between people and machines can all be improved through speech recognition. ASR has seen extensive use recently, including in the design of automobiles, smart phones, video games, internet call centers, and emergency medical situations.

The field of voice recognition faces numerous obstacles despite the availability of numerous techniques. For instance, the extraction and selection of effective features typically has an impact on the classification accuracy in speech emotion identification problems.