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arXiv · 2401.14717

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

Jinhan Wang·Long Chen·Aparna Khare·Anirudh Raju·Pranav Dheram·Di He·Minhua Wu·Andreas Stolcke·Venkatesh Ravichandran
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Abstract

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.

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Jinhan Wang, Long Chen, Aparna Khare, Anirudh Raju, Pranav Dheram, Di He, Minhua Wu, Andreas Stolcke, Venkatesh Ravichandran. 2024-01-26. Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion. https://arxiv.org/abs/2401.14717

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