KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI
arXiv:2510.02327v2 Announce Type: replace Abstract: Real-time speech-to-speech (S2S) models excel at generating natural, low-latency conversational responses but often lack deep knowledge and semantic understanding. Conversely, cascaded systems combining automatic speech recognition, a text-based Large Language Model (LLM), and text-to-speech synthesis offer superior knowledge representation at the cost of high latency, which disrupts the flow of natural interaction. This paper introduces a novel hybrid architecture that bridges the gap between these two paradigms. Our framework processes user
The increasing demand for more natural and efficient human-AI interaction in conversational AI systems is driving innovation in real-time speech processing.
This development addresses a critical trade-off between semantic understanding and low-latency interaction in conversational AI, enabling more effective use cases.
The proposed 'KAME' architecture offers a new paradigm for integrating deep knowledge with real-time speech in AI, potentially accelerating advanced conversational AI applications.
- · Conversational AI developers
- · Generative AI companies
- · Customer service industries
- · Voice assistant providers
- · Legacy cascaded S2S systems
- · Purely real-time S2S systems lacking knowledge
- · Text-based LLM applications without robust S2S integration
Improved human-AI conversation fluidity and depth of understanding in real-time applications.
Accelerated adoption of AI agents in roles requiring complex, real-time verbal interaction.
Increased societal reliance on AI for knowledge retrieval and dialogue in various sectors, leading to new ethical considerations.
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