  We present a series of two studies conducted to understand user's affective
states during voice-based human-machine interactions. Emphasis is placed on the
cases of communication errors or failures. In particular, we are interested in
understanding "confusion" in relation with other affective states. The studies
consist of two types of tasks: (1) related to communication with a voice-based
virtual agent: speaking to the machine and understanding what the machine says,
(2) non-communication related, problem-solving tasks where the participants
solve puzzles and riddles but are asked to verbally explain the answers to the
machine. We collected audio-visual data and self-reports of affective states of
the participants. We report results of two studies and analysis of the
collected data. The first study was analyzed based on the annotator's
observation, and the second study was analyzed based on the self-report.
