标题： 使用大型语言模型的后续对话设备定向语音检测 显示英文标题

标题： Device-Directed Speech Detection for Follow-up Conversations Using Large Language Models

摘要： 与虚拟助手（VA）的后续对话使用户能够无缝地与其交互，而无需在每次查询后重复使用关键词来调用它。 因此，在后续查询中准确检测设备定向语音（DDSD）对于实现自然的用户体验至关重要。 为此，我们探索了大型语言模型（LLM）的概念，并通过提示预训练的LLM或在LLM之上调整二元分类器，基于ASR解码文本推断后续对话。 在此过程中，我们在设计LLM提示时还利用了ASR的不确定性。 我们通过真实世界的后续对话数据集表明，由于联合建模先前的语音上下文和ASR不确定性，这种方法带来了显著的改进（在10%固定误拒绝率下，误报减少20%-40%），相比于单独建模后续对话。 显示英文摘要

摘要： Follow-up conversations with virtual assistants (VAs) enable a user to seamlessly interact with a VA without the need to repeatedly invoke it using a keyword (after the first query). Therefore, accurate Device-directed Speech Detection (DDSD) from the follow-up queries is critical for enabling naturalistic user experience. To this end, we explore the notion of Large Language Models (LLMs) and model the first query when making inference about the follow-ups (based on the ASR-decoded text), via prompting of a pretrained LLM, or by adapting a binary classifier on top of the LLM. In doing so, we also exploit the ASR uncertainty when designing the LLM prompts. We show on the real-world dataset of follow-up conversations that this approach yields large gains (20-40% reduction in false alarms at 10% fixed false rejects) due to the joint modeling of the previous speech context and ASR uncertainty, compared to when follow-ups are modeled alone.