Voice Recognition by Machine Learning Approach

Voice recognition has become a critical component in modern computing systems, enabling machines to process and interpret human speech for a wide range of applications. This study develops a speaker recognition system that leverages Mel Frequency Cepstral Coefficients (MFCCs) for feature extraction and the Gaussian Mixture Model (GMM) for classification. MFCCs capture the unique vocal tract features of individuals, while GMM provides probabilistic modeling for effective classification. The framework is implemented using Python libraries including NumPy, SciPy, scikit-learn, and python_speech_features. Experimental evaluation demonstrates an accuracy of approximately 90%, confirming the reliability of the MFCC-GMM approach in speaker identification tasks. The results suggest that this lightweight framework can be applied effectively in biometric authentication, intelligent voice assistants, and secure telecommunication systems.