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People interacting with voice assistants are often frustrated by voiceassistants' frequent errors and inability to respond to backchannel cues. Weintroduce an open-source video dataset of 21 participants' interactions with avoice assistant, and explore the possibility of using this dataset to enableautomatic error recognition to inform self-repair. The dataset includes clippedand labeled videos of participants' faces during free-form interactions withthe voice assistant from the smart speaker's perspective. To validate ourdataset, we emulated a machine learning classifier by asking crowdsourcedworkers to recognize voice assistant errors from watching soundless video clipsof participants' reactions. We found trends suggesting it is possible todetermine the voice assistant's performance from a participant's facialreaction alone. This work posits elicited datasets of interactive responses asa key step towards improving error recognition for repair for voice assistantsin a wide variety of applications.

Look at Me When I Talk to You: A Video Dataset to Enable Voice Assistants to Recognize Errors