It's a really interesting technical challenge. On the one hand, changing your timbre to another human voice is much more complicated than basic pitch shifting, so we ended up using deep neural networks for a kind of simultaneous speech-recognition and speech-synthesis approach (though training this system to preserve e.g. the emotional complexity of your input speech while still changing out the timbre convincingly is difficult - we use adversarial training on the raw audio waveform, which is powerful but also pretty much unknown territory compared to images).
On the other hand, it's important to run with very low latency on your device while you're playing, which means that we can't simply "throw the biggest network we can" at the problem. So we have a tradeoff between model latency, which is easy to characterize, and audio quality / voice skin plausibility, which is pretty ambiguous and subjective.
Finally, as this kind of tech improves the potential for misuse becomes an important problem, so we need to build in protections (like watermarking the audio) that can help prevent fraud while not compromising the speed of the algorithm or the quality of the output audio.