Blog
Writing on speech-data provenance, evaluation and multilingual deployment for production AI.
Indic TTS Is Getting Better. The Harder Problem Is Knowing Where It Still Fails
Indic TTS is improving quickly, but broad language support does not prove deployment fit. As speech becomes more natural, buyers need more precise evaluation around pronunciation, regional speech, code-switching, structured text and real product conditions.
Why 10,000 Hours of Speech Data Can Still Be Worth Almost Nothing
A speech dataset can look technically strong and still be a bad buy. Before licensing human data, buyers need clear answers on provenance, rights, consent, evidence and intended-use fit
MTurk is shutting down. Voice AI still needs humans
MTurk shuts down on 30 September 2026. But voice AI still needs people to catch what automated scores miss: bad pronunciation, awkward code-switching, broken conversations and voices that just don't feel right
Why More Speech Data Is Not the Same as Better Speech Data
AI teams often think they need more speech data. Usually, they need better speech data: cleaner structure, richer context, stronger speaker variation, and datasets designed around real production behaviour.
Why Voice AI Still Breaks on Real Conversations
Most voice AI systems do not fail because the model is weak. They fail because the data is too clean. Real conversations are messy, multilingual, interrupted, and context-heavy. That is the gap Sonexis is built to solve.