Utterance Information

Utterance information research focuses on understanding how individual utterances within a conversation contribute to overall meaning and impact downstream tasks like sentiment analysis and speech synthesis. Current research emphasizes modeling both intra-utterance (e.g., syntactic structure) and inter-utterance (e.g., contextual relationships, emotional contagion) information, often employing graph neural networks, contrastive learning, and variational autoencoders to capture complex dependencies. These advancements improve the accuracy and naturalness of various applications, including dialogue systems, text-to-speech synthesis, and emotion recognition in conversational AI.