Speaker Identification

Speaker identification (SID) focuses on automatically determining who is speaking in an audio recording, a crucial task with applications in security, forensics, and personalized services. Current research emphasizes improving SID accuracy and robustness across diverse conditions (noise, accents, emotions) using deep learning models, particularly transformer-based architectures and convolutional neural networks, often incorporating techniques like Mel-frequency cepstral coefficients (MFCCs) and vector quantization for feature extraction and improved representation learning. The field's significance lies in its potential to enhance various applications, from improving accessibility of digital archives to developing more secure and personalized voice-activated systems, while also raising important considerations regarding privacy and adversarial attacks.