{'fraction_non_alphanumeric': 0.025936599423631124, 'fraction_numerical': 0.002881844380403458, 'mean_word_length': 4.791666666666667, '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}}
A supervised two-channel learning method for hidden Markov model and application on lip reading

In this paper, a novel two-channel learning method for hidden Markov model (HMM) is proposed. This method is specially designed to train HMMs for fine recognition from similar observations. The prominent features of this method are 1.) the criterion function is based on the difference between training sequences, and 2.) a twochannel structure is adopted to maintain the validity of the HMM. This learning method has been applied on a viseme-level lip reading system. The result shows that the performance of the two channel approach is better than that of the maximum likelihood (ML) estimation.