No Priors: Artificial Intelligence | Technology | Startups
00:12:36 · Sarah Guo, Mati Staniszewski
[00:12:36] S0: So, I wanna ask you a question about this, which relates to quality. I work with a series of companies where we're selling a product to the buyers. There's generally not machine learning scientists. [00:12:49] S1: Right? Right. [00:12:49] S0: And even the scientific community does not have the full suite of evals and benchmarks to understand every domain well. It's a well known problem. But I imagine for a lot of your customers, it's not like they know how to choose good voice. So how do you deal with that problem? Is it like, hey, I make a clone, and that sounds like me, and I believe in it. I'm gonna try all of these different options, or actually, are you teaching people to do eval? [00:13:15] S1: It's a great question because I think there are two big problems. One is how do you benchmark the general space in audio where, like you say, it's so dependent on the specific voice, let alone if you are training it to interactive, then it's even more tricky. And then the second piece, which is as you are working on a specific use case, how you select a voice. So, I'll take the second front first, which is we have a voice sommelier effectively with us. We work with enterprises. We deploy that person to work with them and help them navigate. That person is like a voice coach, has an incredible voice themselves, and now we have like a team under that person that like will partner to help you find what's the right branding of Vanity Boy. [00:13:57] S0: And now you have like the celebrity marketplace.
00:13:57 · Sarah Guo, Mati Staniszewski
[00:13:57] S0: And now you have like the celebrity marketplace. [00:13:59] S1: And now you have a celebrity marketplace to like help you even get get the iconic talent in there, like Sir Michael Caine. That piece was important because, of course, their voice will depend on the use case that you are trying to build, the language. All of that will have an impact of what's the right voice for your customer base. So, we have effectively a voice person helping those companies, and some companies will be very opinionated on what they want. So, they will sometimes select themselves, sometimes give us a brief of, hey, we want a voice that sounds professional, neutral, it's coming. We recently had a company, one of the biggest European companies that gave us a brief, which is very original, that they wanted as robotic voice as possible. [00:14:42] S0: Okay. [00:14:43] S1: It was counterintuitive. Yeah. First of [00:14:46] S0: So you're like, we can't do that anymore. [00:14:48] S1: Almost, but we are like trying to go backwards of like, how do we do that? But I think we got a good result. But recently we had a company in Japan and Korea, where they wanted to serve different voices depending on the customer that's calling in. They have an older population and a very younger population. The younger one, they wanted one of the famous voices in the market that's very excitable and happy, and for the older one, wanted a calm, slow speaking one. We help a lot with that. So, that's on the voice piece, and I do think it's going to be a big and important one.
00:14:48 · Mati Staniszewski, Sarah Guo
[00:14:48] S1: Almost, but we are like trying to go backwards of like, how do we do that? But I think we got a good result. But recently we had a company in Japan and Korea, where they wanted to serve different voices depending on the customer that's calling in. They have an older population and a very younger population. The younger one, they wanted one of the famous voices in the market that's very excitable and happy, and for the older one, wanted a calm, slow speaking one. We help a lot with that. So, that's on the voice piece, and I do think it's going to be a big and important one. [00:15:20] S0: It's like a personalized choice, and then it can even be dynamic in a customer.
00:15:23 · Mati Staniszewski
[00:15:23] S1: Yes. Okay. Exactly. Exactly. And then maybe in the future it's going to be fully depending on your interaction, you will have a voice created as soon as we understand the preferences of what people want. So, you know, like, let's say you're in the evening and you are tired and you want a slightly different... Or maybe not, maybe that's the best focus time that you have a voice that's giving that energy, and probably it's different when you wake up and gives you the morning news of what's happening or what's the weather. So, like, all of those could be different. Yesterday we had a dinner with some of our partners, and one of them... The first thing they said is like, hey, I have a new request for you. I want a New York voice with a Long Island accent, which I never knew as a thing, and it's supposedly a thing. So so we have that. And then on the first piece, I don't... I think it's unsolved problem still, where I think you have good benchmarks, of course, in LLabs. I think in ImageSpace, they are pretty good. In VoiceSpace, you you have, of course, the speech quality, but then so much of whether you like or not the speech depends on the voice that just if you compare model A to model B and you serve them different voices, even if the quality is very different, the voice itself can just make that sort of different. We've seen this, I don't know if you know, artificial analysis benchmarks, I think they're pretty good. Just switching the voice makes such a big advantage.
00:16:43 · Sarah Guo, Mati Staniszewski
[00:16:43] S0: That's so interesting. Yeah, and I wonder if, as you said, this is the most dominant interaction mode we'd had for millennia of all of human history. [00:16:54] S1: I'm biased and self serving, but I think [00:16:56] S0: We're just very sensitive to it, and I think people are gonna be very sensitive to their own personalization as well. [00:17:03] S1: 100%. I think this is also a third piece, maybe is not directly to your to your note, but we've also realized that you have... So you have the benchmarks, you have, like, how do I find the right voice for my audience, but even the understanding of how you describe audio data is still lagging in the industry. Like, when we initially started, we, of course, went into the traditional players for them to help us label not only what was said, so like transcription, but also how it was said, like what are the emotions used, accent. And most people just weren't able to do that work effectively because you kind of need to hear and have like a little bit of a skill set of like how would I describe this specific delivery, so we need to create that ourselves. So I think there is that piece as well of how do you effectively interpret the data of audio in a more qualitative basis. That's, yeah, trickier.
00:17:03 · Mati Staniszewski, Sarah Guo
[00:17:03] S1: 100%. I think this is also a third piece, maybe is not directly to your to your note, but we've also realized that you have... So you have the benchmarks, you have, like, how do I find the right voice for my audience, but even the understanding of how you describe audio data is still lagging in the industry. Like, when we initially started, we, of course, went into the traditional players for them to help us label not only what was said, so like transcription, but also how it was said, like what are the emotions used, accent. And most people just weren't able to do that work effectively because you kind of need to hear and have like a little bit of a skill set of like how would I describe this specific delivery, so we need to create that ourselves. So I think there is that piece as well of how do you effectively interpret the data of audio in a more qualitative basis. That's, yeah, trickier. [00:17:54] S0: Can you talk about what's happening on the agent platform side? Like, what is challenging for, you know, businesses or even creators that are trying to build agents and what the... Maybe what these surprising or high traction use cases are? I think everybody's kind of aware of the idea of agent based customer support, but I imagine you're doing many things beyond that.