  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%.
