Speaker Adaptation

Speaker adaptation in speech and visual processing aims to personalize models for individual speakers, overcoming limitations of generic models that struggle with variations in voice, lip movements, and speaking styles. Current research focuses on efficient adaptation techniques, often employing lightweight modules like Low-Rank Adaptation (LoRA) within larger architectures such as transformers and diffusion models, or leveraging techniques like k-Nearest Neighbors and prototype-based methods. These advancements are significant for improving the robustness and personalization of speech recognition, text-to-speech, lip reading, and other related applications, particularly in low-resource scenarios or for individuals with speech impairments.