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

arXiv: https://arxiv.org/abs/2008.00508 · HTML: https://mlsanity.com/paper/2008.00508/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Bias in Automated Speaker Recognition](https://mlsanity.com/paper/2201.09486/) (2022)
- [Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation](https://mlsanity.com/paper/2105.06598/) (2021)
- [Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models](https://mlsanity.com/paper/2203.15975/) (2022)
- [Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features](https://mlsanity.com/paper/2310.15261/) (2023)
- [FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants](https://mlsanity.com/paper/2109.09958/) (2021)
- [NUANCE: Near Ultrasound Attack On Networked Communication Environments](https://mlsanity.com/paper/2305.10358/) (2023)
- [Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensembles](https://mlsanity.com/paper/2310.11379/) (2023)
- [Not All Speech Is Intent: Adaptive Self-Correcting Inference Layer for Post-ASR False Wake-Up](https://mlsanity.com/paper/2609.12469/) (2026)

## Abstract (arXiv)

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 found in millions of households. For privacy reasons, these speakers analyze every sound in their environment for their respective wake word like ''Alexa'' or ''Hey Siri,'' before uploading the audio stream to the cloud for further processing. Previous work reported on the inaccurate wake word detection, which can be tricked using similar words or sounds like ''cocaine noodles'' instead of ''OK Google.'' In this paper, we perform a comprehensive analysis of such accidental triggers, i.,e., sounds that should not have triggered the voice assistant, but did. More specifically, we automate the process of finding accidental triggers and measure their prevalence across 11 smart speakers from 8 different manufacturers using everyday media such as TV shows, news, and other kinds of audio datasets. To systematically detect accidental triggers, we describe a method to artificially craft such triggers using a pronouncing dictionary and a weighted, phone-based Levenshtein distance. In total, we have found hundreds of accidental triggers. Moreover, we explore potential gender and language biases and analyze the reproducibility. Finally, we discuss the resulting privacy implications of accidental triggers and explore countermeasures to reduce and limit their impact on users' privacy. To foster additional research on these sounds that mislead machine learning models, we publish a dataset of more than 1000 verified triggers as a research artifact.