A current state of the art ai model from Microsoft can achieve acceptable quality with about 3 seconds of audio. Commercially available stuff like eleven labs about 30 minutes. But quality will obviously vary heavily but then again they’re using a low quality phone call so maybe not that important
That’s downright scary :-) I think it took longer in the last Mission Impossible.
30 minutes is still pretty minimal for the kind of targeted attack it sounds like this is used for. I suppose we all need to work with our families on code words or something.
I went in thinking the article was a bit alarmist, but that’s clearly not the case. Thank for the insight.
With that little, they may be able to recreate the timbre of someone’s voice, but speech carries a multitude of other identifiers and idiosyncrasies that they’re unlikely to get with that little audio, like personal vocabulary (we don’t choose the same words and phrasings for things), specific pronunciations (e.g. “library” vs “libary”), voice inflections, etc. Obviously, the more training data you have, the better the output.
A current state of the art ai model from Microsoft can achieve acceptable quality with about 3 seconds of audio. Commercially available stuff like eleven labs about 30 minutes. But quality will obviously vary heavily but then again they’re using a low quality phone call so maybe not that important
That’s downright scary :-) I think it took longer in the last Mission Impossible.
30 minutes is still pretty minimal for the kind of targeted attack it sounds like this is used for. I suppose we all need to work with our families on code words or something.
I went in thinking the article was a bit alarmist, but that’s clearly not the case. Thank for the insight.
With that little, they may be able to recreate the timbre of someone’s voice, but speech carries a multitude of other identifiers and idiosyncrasies that they’re unlikely to get with that little audio, like personal vocabulary (we don’t choose the same words and phrasings for things), specific pronunciations (e.g. “library” vs “libary”), voice inflections, etc. Obviously, the more training data you have, the better the output.