Speaker Representation

Speaker representation focuses on extracting meaningful and discriminative features from speech data to characterize individual speakers. Current research emphasizes unsupervised and self-supervised learning methods, often employing architectures like transformers, conformers, and contrastive learning frameworks, to overcome limitations of data scarcity and improve robustness to noise and speaking style variations. These advancements are crucial for improving performance in various speech applications, including speaker recognition, diarization, voice conversion, and speech synthesis, ultimately leading to more accurate and efficient systems. The development of robust and versatile speaker representations is a key driver of progress in the broader field of speech processing.