Voice assistants in smart speakers analyze every sound in their environment for their wake word , e. This supports users' privacy by only capturing the necessary audio and not recording anything else. The sensitivity of the wake word detection tries to strike a balance between data protection and technical optimization, but can be tricked using similar words or sounds that result in an accidental trigger. In July , we started measuring and analyzing accidental triggers of smart speakers.
We automated the process of finding accidental triggers and measured their prevalence across 11 smart speakers from 8 different manufacturers using professional audio datasets and everyday media such as TV shows and newscasts. Our setup consists of a loudspeaker that is playing media files from a computer. We use light sensors to monitor the LED activity indicators of a group of smart speakers.
All speakers are connected to the Internet over Wi-Fi. To verify the measurement results, we record a video of each measurement via a webcam with a built-in microphone. Moreover, we record network traces to analyze the speakers' activity on a network level.
The entire setup is connected to a network-controllable power socket that we use to power cycle the speakers in case of failures or non-responsiveness. We play a test signal in-between media playbacks to ensure that all smart speakers work and trigger as intended. Our setup was able to identify more than 1, sequences that incorrectly trigger smart speakers.
For example, we found that depending on the pronunciation, Β«AlexaΒ» reacts to the words "unacceptable" and "election," while Β«GoogleΒ» often triggers to "OK, cool. In our paper, we analyze a diverse set of audio sources, explore gender and language biases, and measure the reproducibility of the identified triggers. To better understand accidental triggers, we describe a method to craft them artificially.
