Speaker Independent

Speaker-independent processing aims to develop systems that can accurately analyze speech or visual speech data regardless of the speaker's identity, a crucial step towards robust and widely applicable technologies. Current research focuses on improving model architectures like transformers and graph neural networks, often incorporating techniques like multi-task learning, data augmentation, and adaptive compression to enhance performance and efficiency across diverse acoustic conditions and datasets. This field is vital for advancing applications such as speech recognition, voice conversion, and emotion recognition in real-world scenarios where speaker variability is inherent, leading to more inclusive and versatile systems.