Unified Speech-Text Pretraining for Spoken Dialog Modeling
Heeseung Kim Soonshin Seo Kyeongseok Jeong Ohsung Kwon Jungwhan Kim Jaehong Lee Eunwoo Song Myungwoo Oh Sungroh Yoon Kang Min Yoo
While recent work shows promising results in expanding the capabilities of
large language models (LLM) to directly understand and synthesize speech, an
LLM-based strategy for modeling spoken dialogs remains elusive and calls for
further investigation. This work proposes an extensive speech-text LLM
framework, named the Unified Spoken Dialog Model (USDM), to generate coherent
spoken responses with organic prosodic features relevant to the given input
speech without relying on automatic speech recognition (ASR) or text-to-speech
(TTS) solutions. Our approach employs a multi-step speech-text inference scheme
that leverages chain-of-reasoning capabilities exhibited by the underlying LLM.
We also propose a generalized speech-text pretraining scheme that helps with
capturing cross-modal semantics. Automatic and human evaluations show that the
proposed approach is effective in generating natural-sounding spoken responses,
outperforming both prior and cascaded baselines. Detailed comparative studies
reveal that, despite the cascaded approach being stronger in individual
components, the joint speech-text modeling improves robustness against
recognition errors and speech quality. Demo is available at
https://unifiedsdm.github.io.