I am developing a Flutter app called Talk to Deaf, which aims to enable real-time two-way communication between deaf and hearing users. The app will allow normal users to input text or voice and the deaf user will respond in sign language, while the app will convert those signs back into text or speech.

I am unsure about which type of dataset to use for training my machine learning model: a dataset with individual alphabets (A-Z) or a dataset with complete words/phrases. I want to ensure accurate and smooth communication. Which type of dataset would be more suitable for building a robust real-time sign language interpreter, and what are the trade-offs of each approach?

Any guidance on dataset selection or best practices for training a model for this type of two-way communication app would be highly appreciated.