English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Voice assistants like Amazon's Alexa, Google's Assistant, or Apple's Siri,have become the primary (voice) interface in smart speakers that can be foundin millions of households. For privacy reasons, these speakers analyze everysound in their environment for their respective wake word like ''Alexa'' or''Hey Siri,'' before uploading the audio stream to the cloud for furtherprocessing. Previous work reported on the inaccurate wake word detection, whichcan be tricked using similar words or sounds like ''cocaine noodles'' insteadof ''OK Google.''  In this paper, we perform a comprehensive analysis of such accidentaltriggers, i.,e., sounds that should not have triggered the voice assistant, butdid. More specifically, we automate the process of finding accidental triggersand measure their prevalence across 11 smart speakers from 8 differentmanufacturers using everyday media such as TV shows, news, and other kinds ofaudio datasets. To systematically detect accidental triggers, we describe amethod to artificially craft such triggers using a pronouncing dictionary and aweighted, phone-based Levenshtein distance. In total, we have found hundreds ofaccidental triggers. Moreover, we explore potential gender and language biasesand analyze the reproducibility. Finally, we discuss the resulting privacyimplications of accidental triggers and explore countermeasures to reduce andlimit their impact on users' privacy. To foster additional research on thesesounds that mislead machine learning models, we publish a dataset of more than1000 verified triggers as a research artifact.

Unacceptable, where is my privacy? Exploring Accidental Triggers of Smart Speakers