The field of talking head synthesis is moving towards more realistic and controllable emotional expressions. Recent developments focus on improving emotion accuracy, controllability, and identity preservation. Researchers are exploring various approaches, including the use of variational autoencoders, cross-emotion memory networks, and disentanglement frameworks to generate highly realistic emotional talking heads. Notable papers in this area include RealTalk, which employs a novel framework for synthesizing emotional talking heads with high emotion accuracy, and EDTalk++, which proposes a full disentanglement framework for controllable talking head generation. Other noteworthy papers include CEM-Net, which introduces a cross-emotion memory network to generate emotional talking faces aligned with driving audio, and D^3-Talker, which constructs a static 3D Gaussian attribute field to achieve few-shot 3D talking head synthesis.