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Unsupervised speech-to-speech translation from monolingual data

47 pointsby atg_abhishekover 1 year ago

7 comments

NiloCKover 1 year ago
Looking forward to the debate about real-time translators censoring or altering people&#x27;s speech.<p>Also the debate about whether all human speech need be piped through such a preemptive filter. (actually not looking forward to this one). Suddenly everything that anyone says will be couched with &quot;it is important to consult a professional to ensure safety and compliance with local regulations&quot;.
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xnxover 1 year ago
The idea of translating between languages by sounds is really stretching my mind.
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asveikauover 1 year ago
Machine translation is generally regarded as pretty bad when you speak both languages. I&#x27;ve heard really accurate (in terms of voice and intonation) speech to speech translation which tries preserve the speaker&#x27;s voice, and I fear this will create a false sense of security about accuracy of translation. Similar to when chatgpt gives us confidently false information that is then trusted.
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aleph_minus_oneover 1 year ago
I cannot imagine that this can work well: my personal speaking (and also writing) in my native language is often full of puns that obscure side-meanings or homophones of words, or subly alter common phrases.<p>I cannot even imagine how this translation service might be capable of translating this. A human translator (translating it from speech or text to text) would likely add lots of footnotes with explanations so that the reader is capable of understanding the intended side-meanings that are hard to translate.
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modelessover 1 year ago
Translation is just a special case of general conversation. Just as LLMs can do translation just by asking, we ought to train a general speech-to-speech model for conversation, then we can simply ask it to translate for us.
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camilo0113over 1 year ago
There is still no tool that allows you to translate in real time into another language, from Meet, Google or Zoom
radarsat1over 1 year ago
Amazing. And frustratingly light on details about this magical &quot;MUSE loss&quot; which seems to do a lot of the heavy lifting here, will have to read the paper to see how that works. Anyone have a tldr?