Speech models feel fine until you put them in real conversations
Been working around conversational data recently, and this keeps showing up.
Most speech datasets are too clean compared to actual usage.
In real conversations (especially multilingual ones):
* people interrupt each other
* there’s overlapping speech
* code-switching happens mid-sentence
* context jumps quickly
But training data usually assumes clean turns and stable language.
That mismatch starts to show up fast when you plug models into real workflows.
Feels less like a model limitation and more like a data distribution problem.
Would be interested to hear how others here are handling this, especially if you’re deploying in multilingual or noisy environments
u/Cautious-Today1710 — 12 days ago