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arXiv · 2207.07693

Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction

Sujeong Kim·Abhinav Garlapati·Jonah Lubin·Amir Tamrakar·Ajay Divakaran
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Abstract

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.

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Sujeong Kim, Abhinav Garlapati, Jonah Lubin, Amir Tamrakar, Ajay Divakaran. 2022-07-15. Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction. https://doi.org/10.1109/aciiw52867.2021.9666238

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