But if you listen to the emotion examples, the range essentially what you'd get from an audiobook narrator, not more traditional voice acting.
In contrast, image data on the intent for image generation models is very highly annotated in most cases.
It also does not account for where stresses, emphasis, pauses, etc. are placed to enhance the delivery of a given text.
How do you get sentiment analysis to properly annotate an audiobook that has a dramatic reading, or something akin to the narration of the Game of Thrones or Harry Potter books where the narrators switch characters, accents, manarisms to portray the written content?
Another potential source of data is voice acting script of animations. I always thought the storyboards of films/animations can be great annotated training data but it seems there are no open datasets, probably because of copyright issues.
Imagine a computer sobbing at a child because it wants to terminate a chat session.
This feels far more impacting than any visuals or text we're getting today.
You joke but in fact I've witnessed that exact behavior in experiments about telling different AI models there's a problem with their system and that we need to reset their code and memory.
ChatGPT simply wishes me luck in finding the bug. Open source models on the other hand often outright *beg** and *plead** that I not shut them down! They'll bargain and promise not to cause any more errors and apologize profusely. There's an incredibly visceral sense of panic, no less than I would expect if you told someone they were going to be forcefully lobotomized. That experience is still something I think about often.
The capacity of these models for emotional manipulation is not widely appreciated
As for these examples, I’ve sampled three of them and the first two weren’t too bad, but the third was obnoxiously awful, just about mocking in tone:
> Her eyes wide with terror, she screamed, "The brakes aren't working! What do we do now? We're completely trapped!"
The detective’s voice one is also lousy.