Replication data for: Recognizing Uncertainty in Speech

We address the problem of inferring a speaker's level of certainty based on prosodic information in the speech signal, which has application in speech-based dialogue systems. We show that using phrase-level prosodic features centered around the phrases causing uncertainty, in addition to utterance-level prosodic features, improves our model's level of certainty classification. In addition, our models can be used to predict which phrase a person is uncertain about. These results rely on a novel method for eliciting utterances of varying levels of certainty that allows us to compare the utility of contextually-based feature sets. We elicit level of certainty ratings from both the speakers themselves and a panel of listeners, finding that there is often a mismatch between speakers' internal states and their perceived states, and highlighting the importance of this distinction.

本研究旨在解决基于语音信号中的韵律信息推断说话者确定性程度的问题，该技术可应用于基于语音的对话系统。研究表明，除话语级韵律特征外，使用围绕引发不确定性的短语构建的短语级韵律特征，可提升模型的确定性分类性能。此外，所提模型可用于预测说话者感到不确定的具体短语。本研究的结论依赖于一种新颖的语句征集方法，该方法可生成不同确定性程度的语音语句，使我们能够对比基于上下文的特征集的实用价值。我们分别从说话者本人以及听众专家组处获取确定性等级评分，结果发现说话者的真实内在状态与其被感知到的状态往往存在偏差，并强调了区分这两种状态的重要性。