Prefetching of dialogue responses has been investigated to reduce user-perceived latency (UPL), which refers to the user's waiting time before receiving the system's response, in spoken dialogue systems. To reduce the UPL, it is necessary to predict complete user utterances before the end of the user's speech, typically by language models, to prepare prefetched dialogue responses. In this study, we proposed a prediction confidence model (PCM) that determines whether prefetching is possible or not by estimating the semantic similarity between the predicted complete user utterance and the complete user utterance. We evaluated our PCM based on the differences between the predicted complete user utterance and the complete user utterance.

翻译：为降低口语对话系统中的用户感知延迟（UPL），即用户接收系统响应前的等待时间，对话响应预取技术已被广泛研究。为减少UPL，通常需借助语言模型在用户语音结束前预测完整用户话语，以准备预取的对话响应。本研究提出一种预测置信度模型（PCM），该模型通过评估预测的完整用户话语与实际完整用户话语之间的语义相似度，判断是否可执行预取操作。我们通过分析预测完整用户话语与实际完整用户话语之间的差异，对所提出的PCM进行了性能评估。