{'fraction_non_alphanumeric': 0.017980636237897647, 'fraction_numerical': 0.0, 'mean_word_length': 5.5225225225225225, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improved visual speech synthesis using dynamic viseme k-means clustering and decision trees

We present a decision tree-based viseme clustering technique that allows visual speech synthesis after training on a small dataset of phonetically-annotated audiovisual speech. The decision trees allow improved viseme grouping by incorporating k-means clustering into the training algorithm. The use of overlapping dynamic visemes, defined by tri-phone time-varying oral pose boundaries, allows improved modelling of coarticulation effects. We show that our approach leads to a clear improvement over a comparable baseline in perceptual tests. The avatar is based on the freely available MakeHuman and Blender software components.