A good speech recognition model takes more than 10k hours of labeled speech data to train. The more diversity the better. Most production speech models (Siri, G

A good speech recognition model takes more than 10k hours of labeled speech data to train. The more diversity the better. Most production speech models (Siri, Google) need some domain-specific training as well - for offline or low-resource inference. Additionally, language models that help improve speech recognition accuracy need a fairly decent amount of unsupervised text as well. If you are building a conversational speech model, also consider the data needed to label intents for a large number of phrases. For a language like Norwegian, you are unlikely to find so much labeled and unlabeled data without forking out a large amount of money to literally ask some Norwegians to help. I suspect the work is being done, but it's extremely time consuming and researchers are likely spending time building models that can learn high-quality high-level representations of language that can be transferred to different scripts/languages with a small amount of data.