AI Is Learning to Pick Out Voices from a Crowd’s Chatter
Current voice recognition systems are pretty good—if only one person speaks. But as we’ve said before, understanding voices among more people, which is often known as the cocktail party problem, is tough—even for firms like Amazon, which has amassed gobs of data via its Alexa smart assistant platform.
Now, though, a team of researchers from Mitsubishi Electric Research Laboratory has developed a trick to identify features in a voice that can be used to track a single person in conversation. According to New Scientist, by chopping up audio and identifying how clusters of those features occur over time, it’s possible to trace a voice even in the din of a crowd.
How good is it? Well, results published on the arXiv suggest it can track a single person in conversation even when five people are talking, and can isolate a single voice from two others with 80 percent accuracy. So, not perfect. But it’s a big step toward having Alexa understand you when you ask it to play your new jam over the hubbub of your friends at a dinner party.
Keep Reading
Most Popular
Large language models can do jaw-dropping things. But nobody knows exactly why.
And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.
The problem with plug-in hybrids? Their drivers.
Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.
Google DeepMind’s new generative model makes Super Mario–like games from scratch
Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.
How scientists traced a mysterious covid case back to six toilets
When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.