Smart voice signature: A machine learning approach to speaker identification

Abstract

Speaker identification is recognizing individuals based on their unique vocal characteristics. This paper explores the enhancement of speaker identification systems through machine learning techniques, focusing on Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction and the application of K-Nearest Neighbors (K-NN) and Decision Tree algorithms using the 'Weka' software tool. This study aims to develop a robust system valuable in forensic science, security, and other areas where verifying an individual's claimed identity based on their voice is crucial. By leveraging these sophisticated algorithms, the system enhances the accuracy and efficiency of voice-based identity verification. This research seeks to improve the precision of speaker recognition. It aims to enhance the visualization tools that aid in analyzing voice data, thus facilitating a more intuitive understanding of the results. This integration of advanced machine learning techniques with practical visualization enhancements is expected to broaden the applicability of speaker identification technologies, making them more effective in diverse real-world environments where quick and reliable identification is needed. Through this study, the system is anticipated to contribute significantly to security and forensic analysis, providing a dependable method for identity confirmation in various applications.