Text Independent Speaker Verification

Text-independent speaker verification (TI-SV) aims to identify individuals based on their voice without relying on specific spoken words, a crucial task for security and forensic applications. Current research focuses on improving TI-SV's accuracy, particularly for short utterances, using techniques like deep convolutional neural networks (CNNs), recurrent neural networks (RNNs such as TDNNs), and attention mechanisms to extract robust speaker embeddings. These advancements leverage feature combinations, multi-scale processing, and cross-modal learning (audio-visual) to address challenges like noise, speaking style variations, and limited data. The resulting improvements in accuracy and efficiency have significant implications for various fields, including security systems, law enforcement, and personalized voice assistants.