言语中信任的表现形式研究
A Study on the Manifestation of Trust in Speech
摘要：
研究表明，信任是人机交互的一个重要方面，它直接决定了人们愿意使用系统的程度。自动预测用户对某个系统的信任程度可以用来尝试通过让系统采取相关行动来纠正潜在的不信任，例如道歉或解释其决定。在这项工作中，我们探讨了自动检测的信任程度，用户对虚拟助理（VA）的基础上，他们的语音的可行性。我们开发了一种新的协议，用于收集语音数据的受试者诱导有不同程度的信任的技能的VA。该协议包括一个互动会话，其中要求受试者在虚拟助手的帮助下回答一系列事实问题。为了诱导受试者信任或不信任VA的技能，他们首先被告知VA先前被其他用户评为好或坏;随后，VA根据其声称的能力回答受试者的问题。所有的交互都是基于语音的，受试者和VA进行口头交流，这允许记录在不同信任条件下产生的语音。使用这个协议，我们收集了一个语音语料库在阿根廷西班牙语。我们有明确的证据表明，该协议有效地成功地影响受试者进入所需的精神状态，无论是信任或不信任代理的技能，目前的结果的感知研究的信任程度的专家听众。最后，我们发现，受试者的语音可以用来检测他们使用的是哪种类型的VA，这可以被认为是用户对VA能力的信任的代理，与50%的随机基线相比，准确率高达76%。
英文摘要：
Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use a system. An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions. In this work, we explore the feasibility of automatically detecting the level of trust that a user has on a virtual assistant (VA) based on their speech. We developed a novel protocol for collecting speech data from subjects induced to have different degrees of trust in the skills of a VA. The protocol consists of an interactive session where the subject is asked to respond to a series of factual questions with the help of a virtual assistant. In order to induce subjects to either trust or distrust the VA's skills, they are first informed that the VA was previously rated by other users as being either good or bad; subsequently, the VA answers the subjects' questions consistently to its alleged abilities. All interactions are speech-based, with subjects and VAs communicating verbally, which allows the recording of speech produced under different trust conditions. Using this protocol, we collected a speech corpus in Argentine Spanish. We show clear evidence that the protocol effectively succeeded in influencing subjects into the desired mental state of either trusting or distrusting the agent's skills, and present results of a perceptual study of the degree of trust performed by expert listeners. Finally, we found that the subject's speech can be used to detect which type of VA they were using, which could be considered a proxy for the user's trust toward the VA's abilities, with an accuracy up to 76%, compared to a random baseline of 50%.
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