Duc Canh Nguyen

Papers

Total Citations

H-Index

About

Duc Canh Nguyen is a researcher specializing in human-computer interaction, particularly in the generation of realistic, non-verbal communicative behaviors—such as head motion—from speech. His work bridges the fields of multimodal signal processing, deep learning, and embodied conversational agents. Nguyen’s most cited paper, "Comparing Cascaded LSTM Architectures for Generating Head Motion from Speech in Task-Oriented Dialogs" (2018), systematically evaluates recurrent neural network designs for synthesizing natural head movements in task-oriented dialogues, a critical component for creating believable virtual agents. This contribution addresses the challenge of making spoken interactions with machines feel more human-like, directly impacting the development of social robots and virtual assistants. Although his citation count is currently modest, his research lays foundational groundwork for integrating prosody-driven gestures into conversational AI. Nguyen’s focus on cascaded LSTM architectures highlights his commitment to advancing sequence-to-sequence models for real-time, context-aware behavior generation. His work is particularly relevant for students and researchers exploring how subtle, non-verbal cues can be computationally modeled to enhance user engagement and trust in human-machine communication.