arXiv:2502.14145v3 Announce Type: replace Abstract: Achieving full-duplex communication in spoken dialogue systems (SDS) requires real-time coordination between listening, speaking, and thinking. This paper proposes a semantic voice activity detection (VAD) module as a dialogue manager (DM) to efficiently manage turn-taking in full-duplex SDS. Implemented as a lightweight (0.5B) LLM fine-tuned on full-duplex conversation data, the semantic VAD predicts four control tokens to regulate turn-switching and turn-keeping, distinguishing between intentional and unintentional barge-ins while detecting
Advances in LLM technology and the demand for more natural human-computer interaction are driving innovation in real-time dialogue management, pushing for higher efficiency in full-duplex systems.
This development allows for more seamless and less frustrating interactions with AI, critical for the widespread adoption and integration of AI agents into daily life and professional workflows.
Dialogue systems can now manage turn-taking and interruptions more intelligently, distinguishing intentional user input from accidental noise, leading to vastly improved user experience and operational efficiency.
- · AI assistant developers
- · Customer service industries
- · Speech recognition companies
- · Users of conversational AI
- · Basic VAD module providers
- · Companies relying on half-duplex systems
Full-duplex spoken dialogue systems become significantly more performant and user-friendly.
Increased adoption of AI agents in roles requiring complex verbal interaction due to improved communication fluidity.
The enhanced naturalness of AI interaction could further blur the lines between human and AI communication, impacting social norms and expectations.
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