Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language
Model.
By: Xinyu Zhou, Delong Chen, Yudong Chen,
Communication University of China.
Hong Kong University of Science and Technology.
6th International Conference on Natural Language and Speech Processing.
icnlsp.org/2023welcome
Abstract:
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.