Build a Sign Language Recognizer

Prepare data for modeling

1. Student provides correct alternate feature sets: delta, polar, normalized, and custom.
2. Student passes unit tests.
3. Student provides a reasonable explanation for what custom set was chosen and why (Q1).

Implement model selection techniques

1. Student correctly implements CV, BIC, and DIC model selection techniques in “my_model_selectors.py”.
2. Student code runs error-free in notebook, passes unit tests and code review of the algorithms.
3. Student provides a brief but thoughtful comparison of the selectors (Q2).

Recognize ASL words

1. Student implements a recognizer in “my_recognizer.py” which runs error-free in the notebook and passes all unit tests
2. Student provides three examples of feature/selector combinations in the submission cells of the notebook.
3. Student code provides the correct words within <60% WER for at least one of the three examples student provided.
4. Student provides a summary of results and speculates on how to improve the WER.