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.

我们提出了一种通过将神经声学模型与大型语言模型（LLM）融合的方法，连续预测口语对话中的交替和回应位置。在 Switchboard 人人对话数据集上的实验证明，我们的方法始终优于单模态的基线模型。我们还开发了一种新颖的多任务指令微调策略，以进一步从 LLM 编码的知识中获益，以理解任务和对话语境，从而带来额外的改进。我们的方法展示了结合 LLM 和声学模型在人类和语音启用的 AI 代理之间实现更自然和对话式交互的潜力。