Text-to-Speech

TL;DR for operators A downstream AI model can rescue imperfect upstream output only while enough evidence survives to reconstruct what was lost. Re-Sonance1 makes that boundary unusually clear: after speech recognition, LLM correction lowers Word Error Rate from 21.58 to 13.74 for mild dysarthria and from 23.70 to 17.88 for moderate dysarthria, but severe-case WER rises from 83.77 to 84.40 and Match Error Rate rises from 87.50 to 90.96. Lower error rates mean the reconstructed wording is closer to the intended transcript. ...

Voice demos are easy to fake. Give a model a clean recording, let it read a theatrical sentence, and the result can sound impressive enough for a launch video. That is not the hard part. The hard part is making speech generation behave like an actual product: multilingual, low-latency, emotionally credible, speaker-consistent, and not outrageously expensive to serve. ...