Supervised speech encoders like Whisper sidestep this by training on a massive but narrow task: transcription. Whisper saw 680k hours of labeled speech-text pairs, and it learned extremely good representations for turning speech into text. But its representations are optimized for lexical content, not for the paralinguistic features a translation system needs.
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