Hexatalk using ANN and DNNS

Speaker recognition is an essential aspect of human-computer interaction, with applications in security, personalized services, and more. This project proposes an end-to-end speaker recognition system leveraging Long Short- Term Memory (LSTM) neural networks. Mel-Frequency Cepstral Coefficients (MFCCs) are used as audio features, processed by an LSTM model to classify speakers with high accuracy. The proposed system demonstrates the efficacy of LSTM for temporal feature analysis, achieving robust performance in noisy environments.