Mati Staniszewski: commitment

Transcript context

…It feels like a big part of the magic of Eleven was your voices were much more human-sounding. How did you accomplish that? I'll give you a quick synopsis of how we think about the models on the text-to-speech side today. In any model, you need the architecture, you need to compute, you need data. Architecture innovations were one thing. The data part was the second big thing. With audio, you will have a lot of audio data available, but frequently you will not have it annotated in the right way. You won't have which speaker is speaking when. Some of the "what" is annotated, but the "how" isn't. As we are speaking now, what's the emotions that we use? What are the accents that we use? We would invest a lot internally on effectively creating our own data labellers, our own team, to be able to create those data sets that will be better. That was a combination of, of course, semi-automatic techniques and then manual techniques. Actually, a lot of the models that we did afterwards actually span out from a lot of that research, too. Speech-to-text model initially was a model we did for ourselves because the models on the market just weren't good to annotate that data. Then another brilliant research area on our team was being able to construct it so we could span it out as a model that we brought to the customer. You've just been doing useful stuff in voice, and that has emerged with a whole bunch of products that you might not have expected because you find you're building useful stuff.…

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