Year: 2025
Type: article
Abstract: 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.
Authors: M Ravi, A Obulesu, CH Vinod Vara Prasad, N Abhishek, N Rithish Reddy, V Anil Chary
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409967109