Deep learning models tailored for the Estonian language's phonetic structure.

The systems show high reliability in controlled, professional environments like parliament, which often feature clear, structured speech. Error Rates:

Approximately 8.5% word error rate , which is considered highly effective for live AI-driven content.

Automated systems in Estonia utilize speech-to-text engines based on Kaldi-based TDNN-F models , which are specifically trained to recognize Estonian speech segments, punctuation, and normalize text.

While improving, AI can sometimes struggle with specialized terminology or proper nouns if they are not present in the training dataset.