KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI

• URL: http://arxiv.org/abs/2510.02327v1
• Date: Fri, 26 Sep 2025 00:46:34 GMT
• Title: KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI
• Authors: So Kuroki, Yotaro Kubo, Takuya Akiba, Yujin Tang,
• Abstract summary: Real-time speech-to-speech (S2S) models excel at generating low-latency conversational responses but often lack deep knowledge and semantic understanding.<n>C 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.<n>This paper introduces a novel hybrid architecture that bridges the gap between these two paradigms.
• Score: 14.667102744113295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 speech through an S2S transformer for immediate responsiveness while concurrently relaying the query to a powerful back-end LLM. The LLM's text-based response is then injected in real time to guide the S2S model's speech generation, effectively infusing its output with rich knowledge without the full latency penalty of a cascaded system. We evaluated our method using a speech-synthesized variant of the MT-Bench benchmark that consists of multi-turn question-answering sessions. The results demonstrate that our system substantially outperforms a baseline S2S model in response correctness, approaching that of a cascaded system, while maintaining a latency on par with the baseline.

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