Spoken Conversation

Spoken conversation research aims to understand and model the complexities of human dialogue, focusing on accurate representation and generation of speech in various contexts. Current efforts concentrate on bridging the gap between written and spoken language using techniques like data augmentation, incorporating paralinguistic information into large language models, and developing robust dialogue state tracking systems often employing neural network architectures such as Bi-LSTMs. This research is crucial for improving human-computer interaction, particularly in task-oriented dialogue systems and applications like virtual assistants and therapeutic interventions, where accurate understanding and natural response generation are paramount.