Implementation of a voice recognition system using machine learning classifiers

Abstract

Human voice recognition is a modern application and trend in the fields of physical signal processing, human-computer interaction, and biometric security. This study presents an advanced methodological framework for processing audio signals to improve the accuracy and efficiency of voice recognition systems by integrating Mill Frequency Circular Coefficients (MFCC) spectroscopy with deep learning techniques. The methodology begins with feature extraction, where MFCC parameters are used to represent the spectral structure of the voice in a way that mimics the perceptual characteristics of the human auditory system, enabling a highly condensed and meaningful representation of the audio information. Subsequently, deep learning models—specifically convolutional neural networks (CNNs)—are employed to analyze these features and extract distinctive voice patterns.

simulation results indicate that the integration of MFCC and CNN models achieves significant superiority over traditional voice recognition methods, particularly in environments with high noise levels or high speaker characteristics. The proposed methodology also demonstrates greater generalizability and improved model performance in real-world applications.