The recent developments in the field of emotion recognition across speech and facial expressions are notably advancing through innovative approaches that leverage large language models (LLMs) and deep learning techniques. A significant trend is the integration of LLMs to enhance the accuracy and contextual understanding of emotional states, particularly in speech emotion recognition (SER). This is evidenced by methods that refine transcriptions and utilize contextual utterance analysis to improve prediction accuracy, showcasing a move towards more nuanced and reliable emotion detection. Additionally, there is a growing emphasis on fairness and bias mitigation in facial expression recognition (FER), with researchers employing latent space representation learning to address demographic biases and improve model fairness. The introduction of soft-labeling techniques for facial expression datasets, such as AffectNet+, represents a novel approach to capturing the complexity of human emotions more accurately. Furthermore, the field is witnessing a critical examination of the impact of inference acceleration strategies on the bias of LLMs, highlighting the need for comprehensive evaluations to ensure equitable outcomes. Overall, these advancements are pushing the boundaries of emotion recognition technology, making it more accurate, fair, and capable of handling the intricacies of human emotional expressions.