{'fraction_non_alphanumeric': 0.031023784901758014, 'fraction_numerical': 0.012409513960703205, 'mean_word_length': 5.205128205128205, '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}}
Spoken Character Recognition

We investigated the problem of spoken character recognition on the alphabets, numbers, and special characters. We used the Mel-Frequency Cepstral Coefficients (MFCC) as the feature points to characterize the spoken characters, and then reduced the dimensionality of the feature vector by applying the Principal Component Analysis. Four different machine learning algorithms were trained using the dimension-reduced feature vectors, and we compared their performance. For the alphabet set, we realized that many of the letters with similar sound structures were confused for each other, so we instead took a twolayer approach: first determine which “character set” an input is in, and then classify the sample within the set. We used the K-Means algorithm to determine the character set. For the best performance, we achieved 58.02% accuracy for the alphabets, 92.05% accuracy for the numbers, and 82.85% accuracy for the special letters.