mariano54
Joined 12 years ago
Joined 12 years ago
It doesn't reach the frontier in either latency or accuracy for ai multilingual conversations.
1. The response latency / patience can be configured in the quick settings (tap the gear during the call).
2. There already is a bias in transcription. It's something we're actively improving, multilingual transcription isn't a solved problem yet
3. It is supposed to correct you actively if you have corrections enabled (check the settings). But we received feedback that it's not enough, so we will ramp it up soon
There are incorrect reading or Chinese readings occasionally, but you can tell when that happens due to the furigana being different
We are aiming to create a long term companion/tutor that gets to know you more and more, and can create customized curriculums, lessons, etc.
Also, having the AI voice tutor as our main feature allows us to iterate quickly, and be well positioned for future improvements in AI models.
As for marketing and GTM, we're in the super early stages, and there is definitely a lot of competition out there, it won't be easy.
Intelligence is super key here, especially as the context size gets larger (due to memory) and intelligence degrades.
Another major issue is TTS voice quality, but this seems to be improving a lot for small local models.
EDIT: You're right, latency is also a big deal. You need to get each piece under a second, and the LLM part would be especially slow on mobile devices.
We focused on testing and tweaking the most popular ones, we have not tested some of the niche ones. We have removed languages that users have told us have major issues, but there are still some left.
The voices are due to the quality of the TTS services that we use. Openi, 11labs, minimax. Some services don't have many or even 1 good voice. We will add more over time
Sesame also passes in the users voice into the TTS model so that it can vibe well with the users tone and mood, whereas we are just using raw TTS. Their latency is also very low, but this is not quite suitable for language learning.
In the future we hope to move to full voice to voice models, once those become mature and intelligent enough.