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Wowsers. This is a step change in quality compared to SOTA. I suspect that without evaluating samples as a correlated group, distinguishing between the generate
by webmaven 4y ago
Wowsers. This is a step change in quality compared to SOTA. I suspect that without evaluating samples as a correlated group, distinguishing between the generated samples and those recorded from a human will be little better than a coin toss.
And even when evaluating these samples as a group, I may be imagining the distinctions I am drawing from a relatively small selection that might be cherry-picked. Nevertheless:
The generated samples are more consistent as a group, and more even in quality, with few instances of emphasis that seem (however slightly) out of place.
The recorded human samples vary more between samples (by which I mean the sample as a whole may be emphasized with a bit of extra stress or a small raising or lowering of tone compared to the other samples), and within the sample there is a bit more emphasis on a word or two or slight variance in the length of pauses, mostly appropriate in context (as in, it is similar to what I, a non-professional[0], would have emphasized if I were being asked to record these).
In general for a non-dramatic voiceover you want to maintain consistency between passages (especially if they may be heard out of order) without completely flattening the in-passage variation, but tastes vary.
Conclusion: For many types of voice work, these generated samples are comparable in quality or slightly superior to recordings of an average professional. For semi-dramatic contexts (eg. audiobooks) the generated samples are firmly in the "more than good enough" zone, more or less comparable to a typical narrator who doesn't "act" as part of their reading.
[0] Decades ago in Los Angeles I tried my hand at voiceover and voice acting work, but gave up when it quickly became clear that being even slightly prone to stuffy noses, tonsillitis and sore throats was going to pose a major obstacle to being considered reliable unless I was willing to regularly use decongestants, expectorants, and the like.
- axiolite 4y ago> distinguishing between the generated samples and those recorded from a human will be little better than a coin toss. The synthesis still doesn't know where to place emphasis. You may be unable to distinguish between POOR human voice work and TTS, but not GOOD human reading. Not a big improvement over some existing TTS. Try e.g. Michelle at: https://cloudpolly.berkine.space/ https://cloudpolly.berkine.space/
- deleted 4y ago[deleted]
- ebjaas_2022 4y agoYou can hear that the human readers place emphasis based upon an understanding of the meaning of the text that they're reading, and also based upon an understanding of the humans at the receiving end. It seeps through that they're human. The AI generated samples are good, but they're bland in comparison. The human emphasized words are typically not emphasized in the AI generated samples.
- SemanticStrengh 4y agoI'd be nice to be able to tag specific words for emphasis in a sentence, where the tagging process would be made via semantic NLU tasks and the voice alteration by the TTS model
- noir_lord 4y agoThat'd be interesting because it'd split the problem into "parse and highlight what should be emphasised" and "do the TTS".
- gattilorenz 4y agoI think there's alrrady research for "TTS after NLG" that does this, since a NLG system can export meta-info about emphasis, in addition to the text (at least in case of non-end2end NLG systems). Whether that makes a big difference in practice, I don't know.
- webmaven 4y agoI hear more stress by the human reader, but it isn't always in the appropriate spot, IMO.
- ebjaas_2022 4y agoThat might be true. But I think a good reader, such as a news anchor or a voice actor, will know where to put the emphasis and the pauses, in order to help the listener along. It's value-adding. I think most people who do it professionally will have this capacity.