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The model learns by pulling representations of the same audio together and pushing different audios apart. It works well, but the distortions (called augmentations) are designed by humans, and they implicitly decide what the model treats as “irrelevant variation.” If you always augment with pitch shifts, the model learns to ignore pitch, but this might be what you don’t want for a translation encoder that needs to preserve the speaker’s intonation.

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马琳,独立研究员,专注于数据分析与市场趋势研究,多篇文章获得业内好评。

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