This paper explores the potential of constructing an AI spoken dialogue
system that "thinks how to respond" and "thinks how to speak" simultaneously,
which more closely aligns with the human speech production process compared to
the current cascade pipeline of independent chatbot and Text-to-Speech (TTS)
modules. We hypothesize that Large Language Models (LLMs) with billions of
parameters possess significant speech understanding capabilities and can
jointly model dialogue responses and linguistic features. We conduct two sets
of experiments: 1) Prosodic structure prediction, a typical front-end task in
TTS, demonstrating the speech understanding ability of LLMs, and 2) Further
integrating dialogue response and a wide array of linguistic features using a
unified encoding format. Our results indicate that the LLM-based approach is a
promising direction for building unified spoken dialogue systems.

本文研究构建一种 “思考如何回应” 和 “思考如何讲话” 的人工智能口语对话系统，相比当前独立的聊天机器人和文本到语音（TTS）模块的级联流水线，更贴近人类语音生成过程。研究使用拥有数十亿参数的大型语言模型（LLMs），展示其在语音理解能力上的潜力，并通过统一的编码格式进一步整合对话回应和各种语言特征。实验结果表明，基于 LLM 的方法是构建统一口语对话系统的一种有前景的方向。