Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion

We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM). Experiments on the Switchboard human-human conversation dataset demonstrate that our approach consistently outperforms...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Wang, Jinhan, Long, Chen, Khare, Aparna, Raju, Anirudh, Dheram, Pranav, He, Di, Wu, Minhua, Stolcke, Andreas, Ravichandran, Venkatesh
Format: Article
Language:English
Subjects:
Large language models
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Summary:We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM). Experiments on the Switchboard human-human conversation dataset demonstrate that our approach consistently outperforms the baseline models with single modality. We also develop a novel multi-task instruction fine-tuning strategy to further benefit from LLM-encoded knowledge for understanding the tasks and conversational contexts, leading to additional improvements. Our approach demonstrates the potential of combined LLMs and acoustic models for a more natural and conversational interaction between humans and speech-enabled AI agents.
ISSN:2331-8422