Skip to Content
Artificial intelligence

A New Trick Can Spoof a Speech Recognition AI Every Time

January 10, 2018

Given an audio waveform, researchers can now produce a virtually identical version that makes speech-recognition software transcribe something else entirely.

Backstory: Adversarial examples have fooled plenty of computer-vision algorithms. While all neural networks are susceptible to such attacks, researchers have had less success with audio. Previous attacks were only able to make subtle tweaks to what the software hears.

What’s new: Berkeley researchers showed that they can take a waveform and add a layer of noise that fools DeepSpeech, a state-of-the-art speech-to-text AI, every time. The technique can make music sound like arbitrary speech to the AI, or obscure voices so they aren’t transcribed.

Brace for annoyance: Imagine playing a music video from YouTube on your speakers and having Alexa “hear” an order for two tons of creamed corn. Welcome to AI attack hell.

Deep Dive

Artificial intelligence

What does GPT-3 “know” about me? 

Large language models are trained on troves of personal data hoovered from the internet. So I wanted to know: What does it have on me?

DeepMind has predicted the structure of almost every protein known to science

And it’s giving the data away for free, which could spur new scientific discoveries.

An AI that can design new proteins could help unlock new cures and materials 

The machine-learning tool could help researchers discover entirely new proteins not yet known to science.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.