{'fraction_non_alphanumeric': 0.026694045174537988, 'fraction_numerical': 0.0, 'mean_word_length': 5.25, '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}}
Grapheme-to-phoneme conversion methods for minority language conditions

This study attempts to investigate the grapheme-to-phoneme conversion approaches for minority language conditions. Instead of isolated-word data for major languages, sentence-form data is defined to be a proper form of training data for minority languages. Joint-multigram Model and Hidden Markov Model were examined in this study. The “treat-sentence-as-word” training method and the forced-alignment process were proposed to extend the Joint-multigram Model and the Hidden Markov Model respectively to meet the minority language conditions. Results get from the sentence-form training data using our proposed methods are as good as the results get from the isolated-word training data using previous proposed methods. The Joint-multigram Model performs better for well-designed training data, while the Hidden Markov Model has more error capacity and is more proper for minority language conditions.