00:19:53 · John Collison, Mati Staniszewski
[00:19:53] S0: Yes. [00:19:54] S1: Passes the voice tune test. Works well. And, like, let's take another, like, spectrum of that, an interactive gaming experience, like, a truly interactive as you would have with another human in that game, it's, like, you know, so hard and and and and further out there, we haven't passed it yet there. [00:20:10] S0: Yes. Yes. [00:20:10] S1: Sounds good. Yeah. But I think that's a combination of, like, know, even like a simpler version of within that. Sometimes you might give a response immediately back. Sometimes you need a tool call to get additional information from the database, how you orchestrate that. So, like, that's probably the most common thing we see as we work with some of the companies out there is you want those systems to orchestrate extremely well where if it's a conversational use case, pretty simple, you can route the agent to speak with, but if you need to authenticate, if you need to pull additional information from the database, what do you do? How do you handle that graciously? [00:20:41] S0: That's really tricky. [00:20:42] S1: And, yeah, and and to that extent, I would grade that. That's just getting getting getting there. Yes. And we'll hopefully see that we... Our goal is to, like, pass the voice chewing test in all those cases or the chewing test for all conversational agents outside of voice two, and I hope we will all be there in the in the next year or so.
00:21:02 · John Collison
[00:21:02] S0: For subscription businesses, a lot of revenue is lost in that last few seconds before the checkout. Someone has to get up and find their wallet or they mistype their card number or they hit an error. They just get up and you lose the sale. For a company like ElevenLabs, adding hundreds of thousands of subscribers, even a tiny bit of friction like that, it would really add up. But that's why ElevenLabs uses Link from Stripe. Customers save their details once, and then they can check out in seconds across more than a million businesses with saved credentials. So if you want a faster checkout for your customers, you should turn on Link from Stripe. Are you guys working on personalized voice transcription? Where it feels like part of the way we're making it hard for ourselves is when I speak to Siri, I have a bit of an accent, and so it sometimes has a hard time understanding me, but my accent doesn't change. And so it could just get good at listening to John. But my understanding is it's not. It's just like running the global voice recognition model. And I'm guessing it's the same for ElevenLabs where you're running the global voice recognition model. But, again, you have an accent. And so if someone's understand... Like, if you walked up to someone in a coffee shop and said two words, they might have a hard time understanding it because they'd like... They're not putting it through their Mati Polish accent filter. And so where is this going with, like, actually interpreting the person that you know to exist on the other side?
00:22:24 · Mati Staniszewski, John Collison
[00:22:24] S1: Yeah. I have a I have a very tricky one to detect. So my voice is frequently used in the test Ah, [00:22:29] S0: you're from the test suite. [00:22:30] S1: Yeah. Text to speech, for speech to text, for, like, everything. Yeah. It's it's pretty... [00:22:34] S0: But, again, trying to parse your voice in a global model is just making life hard. It's like have a Mati specific model. [00:22:40] S1: Yeah. So so on the on the speech to text, like transcription, exactly. Like, the big part now that we are bringing in is you have two parts. One, effectively like a person or a voice specific detection, which is true for the accent side, but it's also true for a crowded room. That's where we incredible research team that's able to continually do both the accuracy high, but also Yes. Also also, like, add things like speaker detection, of course, noise reduction. But then the second part is also keyword detection. So there are specific words that you would want to say in those settings that you want to effectively monitor for. So we spoke about, you know, like, let's say I'm going to the coffee shop and order things. The set of actions the coffee shop would expect me to do [00:23:26] S0: is It's like there's a... You know, they can just listen out for the the coffee words.
00:23:26 · John Collison, Mati Staniszewski
[00:23:26] S0: is It's like there's a... You know, they can just listen out for the the coffee words. [00:23:30] S1: Exactly. And then try to, like, match it to the closest proximity. So both things will help. A setup where you have my voice, perfect, you can decode it and code it on that. If you don't have my voice or even if you want to double amplify it, we already support effectively a keyword detection, which is useful for, like, real time setting and async settings. So Yeah. Back to, Cheeky Pint transcription [00:23:54] S0: Yeah. [00:23:54] S1: You could effectively pre pre generate that from the previous podcast and, like, look for a set of words that you would use Yes. Traditionally in that. [00:24:02] S0: And so how hard... Okay. So you do the keyword detection already, but how hard are the... I want to get superhuman transcription performance by feeding it an hour of Mati audio before it listens to Mati, and then it should be able to do a much better job transcribing. Is that just a really hard research problem? [00:24:18] S1: No. Solvable. It's solvable. We think we can we can we can roll it out in one of the next versions, which is, like, hopefully in the next in the next month. [00:24:26] S0: Oh, so you think this year, you're doing Yeah. For sure. Person specific transcription. [00:24:30] S1: Person specific transcription. Like, we can already diarize speakers extremely well. Like, if we are speaking, we can, of course, dissimilate who is speaking when. [00:24:38] S0: Yes.
00:24:38 · John Collison, Mati Staniszewski
[00:24:38] S0: Yes. [00:24:39] S1: Which is like, in transcription side, apart from accuracy, diarization is one of the harder problems. And We do that extremely well. Now it's going to be effectively what you're saying, fine tuning based on the speaker that I want to listen to, which we know will be important. In healthcare setup, such an important part. You're in an operating room, you're a doctor, you want to say a command, then you want to really be able to listen to that one person specific piece. [00:25:03] S0: Yes. [00:25:03] S1: You have a hardware device at home, let's say it's a pilot, that helps you control the the the TV. Here too, you will want that to listen to you versus, let's say, the family roaming around, or maybe you want it to everyone. So, like, you you could decide that, but in in many cases, you you want to be able to specify that. [00:25:20] S0: Okay. That's really exciting. [00:25:22] S1: It's great because there's, like, still so many unsolved research problems that yeah. We They're, there's just [00:25:28] S0: breakthrough after breakthrough coming in the in the domain of voice models. How about on the flip side when it comes to speech generation? You know, the Zoom touch up my appearance feature? Yeah. Yeah. I've always thought about that in the context of voice where should you offer a deaccenting filter for voices? Or, like, even this one podcast that I like to listen to put the voice a little mumbly, and I always thought they should put it through a demumbling filter just to, like, make Or slow [00:25:54] S1: it down, slow it down.
00:25:54 · Mati Staniszewski, John Collison
[00:25:54] S1: it down, slow it down. [00:25:55] S0: Make the enunciation a little better. Yeah. But all these things, again, like photoshopping an image Yeah. There's no reason that the... Like, have you thought about voice to voice, basically, rather than voice to text or text to voice?