The field of audio-driven talking head generation is moving towards more realistic and expressive models, with a focus on capturing nuanced emotional cues and dynamic changes in actions or attributes. Recent developments have introduced novel frameworks that integrate multi-modal emotion embedding, explicit AU-to-landmark modeling, and keyframe-aware diffusion. These advancements have led to significant improvements in lip synchronization accuracy, quantitative image quality, and perceptual realism. Noteworthy papers include Audio-Driven Universal Gaussian Head Avatars, which proposes a universal speech model that directly maps raw audio inputs into a latent expression space, and SynchroRaMa, which introduces a novel framework that integrates multi-modal emotion embedding and scene descriptions generated by Large Language Model. KSDiff is also notable for its Keyframe-Augmented Speech-Aware Dual-Path Diffusion framework, which disentangles expression-related and head-pose-related features and predicts the most salient motion frames.