[nateb2022]

> This generic answer from Wikipedia is not very helpful in this context.

Actually, the general definition fits this context perfectly. In machine learning terms, a specific 'speaker' is simply a 'class.' Therefore, a model generating audio for a speaker it never saw during training is the exact definition of the Zero-Shot Learning problem setup: "a learner observes samples from classes which were not observed during training," as I quoted.

Your explanation just rephrases the very definition you dismissed.

[woodson]

From your definition:

> a learner observes samples from classes which were not observed during training, and needs to predict the class that they belong to.

That's not what happens in zero-shot voice cloning, which is why I dismissed your definition copied from Wikipedia.

[nateb2022]

> That's not what happens in zero-shot voice cloning

It is exactly what happens. You are confusing the task (classification vs. generation) with the learning paradigm (zero-shot).

In the voice cloning context, the class is the speaker's voice (not observed during training), samples of which are generated by the machine learning model.

The definition applies 1:1. During inference, it is predicting the conditional probability distribution of audio samples that belong to that unseen class. It is "predict[ing] the class that they belong to," which very same class was "not observed during training."

You're getting hung up on the semantics.

[woodson]

Jeez, OP asked what it means in this context (zero-shot voice cloning), where you quoted a generic definition copied from Wikipedia. I defined it concretely for this context. Don't take it as a slight, there is no need to get all argumentative.