Been looking closely at speech datasets lately, and something feels off.
Most of what’s used to train models is way too clean.
No interruptions.
No overlap.
Hardly any code-switching.
But that’s not how people actually speak, especially in India.
Real conversations are messy. People switch between Hindi and English mid-sentence, talk over each other, drop context, pick it back up.
Feels like models aren’t failing because of architecture, but because the data doesn’t reflect reality.
Curious how others here are dealing with this.
Are you seeing the same gap in real-world performance?
submitted by /u/Cautious-Today1710
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