Automatic Speaker Recognition System

The “Automatic Speaker Recognition” (ASR) project focuses on developing a robust system capable of recognizing speakers based on unique characteristics in their speech. This system utilizes key techniques such as Mel Frequency Cepstral Coefficients (MFCCs) for feature extraction and Vector Quantization (VQ) using the KMeans clustering algorithm for pattern recognition. During the training phase, speech samples from various speakers are processed to build individual speaker-specific codebooks. In the testing phase, unknown speech samples are matched against these codebooks to identify the speaker. The project includes validation using real-life speech data from multiple speakers, ensuring the system’s accuracy and reliability under varied conditions. This project demonstrates practical applications in areas such as telephone banking, remote computer access security, and identity verification, highlighting the effectiveness of ASR systems in enhancing security and user authentication processes.

The final presentation can be found here.

The code repository for this project can be found here.