Uzbek Voice AI — Low-Resource Language Speech Research & Realtime Prototype

Research and working prototypes for voice AI in Uzbek, a language the major speech vendors barely support.

Why this is hard

English voice AI is a solved-ish problem with a dozen good vendors. Uzbek is not. Recognition accuracy is poor out of the box, the language switched between Latin and Cyrillic scripts within living memory, morphology is heavily agglutinative so word-level models fragment badly, and code-switching with Russian is normal in everyday speech. Off-the-shelf pipelines fall over on all four.

What I worked on

• Streaming STT evaluation across available providers and open models for Uzbek, measured on real conversational audio rather than clean read speech.
• TTS quality and intelligibility comparison, including handling of Latin/Cyrillic script variation and loanwords.
• Realtime latency optimisation for the full voice loop under conditions where the model is slower and less certain than it would be in English.
• Multilingual assistant design that degrades gracefully: detecting language and code-switching mid-utterance, and falling back sensibly instead of confidently transcribing nonsense.
• Dataset and evaluation workflow so quality claims are measured rather than asserted.

Why it matters

This is the same engineering problem every non-English market has. The techniques transfer directly to any low-resource or code-switching voice deployment.

Stack

Streaming STT, TTS, realtime audio pipelines, Python, evaluation tooling.

Live demo, GitHub and architecture notes: https://www.twine.net/signin