Reproduce “LTS-VoiceAgent: A Listen-Think-Speak Framework for Efficient Streaming Voice Interaction via Semantic Triggering and Incremental Reasoning” on OpenResearch
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About this paper
Wenhao Zou, Yuwei Miao, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Jingwen Xu
LTS-VoiceAgent introduces a "Listen-Think-Speak" framework designed for efficient streaming voice interaction by integrating a dynamic semantic trigger and a dual-role stream orchestrator into cascaded LLM systems. The framework achieves sub-second Time-to-First-Sentence and significantly higher reasoning accuracy compared to existing streaming baselines, demonstrating robust performance against natural speech disfluencies.
Key insights
- Intelligently determining "when to think" based on semantic completeness, rather than fixed intervals or voice activity, is crucial for improving computational efficiency and preventing logical inconsistencies in streaming LLM interactions.
- Asynchronous, dual-role processing (Thinker/Speaker) enables cascaded LLMs to achieve both low latency and deep reasoning by allowing background state refinement while simultaneously generating foreground responses.
- Effective handling of natural speech disfluencies, such as self-corrections and hesitations, requires robust mechanisms for thought rollback and state preservation to maintain reasoning quality and efficiency.
Summary by alphaXiv.
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