> You’d want parallel speech data: the same utterance spoken in English, Portuguese, Japanese, Arabic, Mandarin, and dozens more languages.
There is no such things as parallel speech data. The idea that parallel text is a thing is dubious in the first place, like there's "translation tone" in Japanese that refers to the voice-of-text distinct to translated Western texts. The entire concept of translation between distinct human languages is a thing born out of practical necessity rather than something with a concrete theoretical basis.
The Babel fish in The Hitchhiker's Guide to the Galaxy is supposed to be mind-reading. They don't rely on spoken utterances at all, but they read the minds of creatures within its telepathic range and feed the language portions of it into its host's brain, thereby achieving zero-lag realtime translation. This may or may not mean the author Douglas Adams knew that general universal translation is impossible, but it makes the fictional fish not contradictory to the reality that zero-lag interpretation through audio is basically impossible.
You CAN probably do a parallel speech voice to voice if you're okay with something like 30s delay. But if you want a voice-to-voice no-pause zero-delay, I mean, people sometimes think as they speak, everyone can do, yet not everyone speaks languages with same word orders, you literally need a crystal ball that reads dices before they're even rolled.
As somebody fluent in quite a few languages, language definitely affects even the way one thinks about things. Translation will always be imperfect because it's between different conceptual spaces, not some sort of mechanical replacement.
Very cool. I learned something new about why EMA (exponential moving average) is needed:
> EMA-based training dynamics like JEPA’s don’t optimize any smooth mathematical function, yet they provably converge to useful, non-collapsed representations.
All the papers say EMA avoids “representation collapse” without justifying it. Didn’t realize there were any theoretical results here.
Very cool work! We spend a lot of time thinking about "robust representations" in the video space.
Are there any alternative ideas to JEPA right now, when it comes to speech encoding that couples meaning and sound? Curious to learn more about journey from the problem space to solution space (JEPA).
For context, in our domain video-JEPA hasn't proved to be as helpful as one would have hoped. It's decent at high level semantics (e.g. action detection) but doesn't capture enough "detail" (intentionally so) to be used as a powerful enough encoder (or regularizer). Might be just because the research models are too small / haven't been trained on sufficiently large volumes of data, yet.
I was just thinking of something along the same lines, but for something much sillier. Use self-supervised JEPA to make a color visualizer for audio -- synesthesia.
Was wondering if you guys have explored leJEPA and SIGReg?
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[ 15.2 ms ] story [ 58.9 ms ] threadThere is no such things as parallel speech data. The idea that parallel text is a thing is dubious in the first place, like there's "translation tone" in Japanese that refers to the voice-of-text distinct to translated Western texts. The entire concept of translation between distinct human languages is a thing born out of practical necessity rather than something with a concrete theoretical basis.
The Babel fish in The Hitchhiker's Guide to the Galaxy is supposed to be mind-reading. They don't rely on spoken utterances at all, but they read the minds of creatures within its telepathic range and feed the language portions of it into its host's brain, thereby achieving zero-lag realtime translation. This may or may not mean the author Douglas Adams knew that general universal translation is impossible, but it makes the fictional fish not contradictory to the reality that zero-lag interpretation through audio is basically impossible.
You CAN probably do a parallel speech voice to voice if you're okay with something like 30s delay. But if you want a voice-to-voice no-pause zero-delay, I mean, people sometimes think as they speak, everyone can do, yet not everyone speaks languages with same word orders, you literally need a crystal ball that reads dices before they're even rolled.
Which is why translation models such as the one from the article are no longer trained that way.
> EMA-based training dynamics like JEPA’s don’t optimize any smooth mathematical function, yet they provably converge to useful, non-collapsed representations.
All the papers say EMA avoids “representation collapse” without justifying it. Didn’t realize there were any theoretical results here.
Are there any alternative ideas to JEPA right now, when it comes to speech encoding that couples meaning and sound? Curious to learn more about journey from the problem space to solution space (JEPA).
For context, in our domain video-JEPA hasn't proved to be as helpful as one would have hoped. It's decent at high level semantics (e.g. action detection) but doesn't capture enough "detail" (intentionally so) to be used as a powerful enough encoder (or regularizer). Might be just because the research models are too small / haven't been trained on sufficiently large volumes of data, yet.