{'fraction_non_alphanumeric': 0.028669724770642203, 'fraction_numerical': 0.0045871559633027525, 'mean_word_length': 4.97945205479452, '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}}
Sopoken Term Detection Based on a Syllable N-gram Index at the NTCIR-11 SpokenQuery&Doc Task

For spoken term detection, it is crucial to consider out-ofvocabulary (OOV) and the mis-recognition of spoken words. Therefore, various sub-word unit based recognition and retrieval methods have been proposed. We also proposed a distant n-gram indexing/retrieval method for spoken queries, which is based on a syllable n-gram and incorporates a distance metric in a syllable lattice. The distance represents condence score of the syllable n-gram assumed the recognition error such as substitution error, insertion error and deletion error. To address spoken queries, we propose a combination of candidates obtained through some ASR systems which are based on syllable or word units. We run some experiments on the NTCIR-11 SpokenQuery&Doc Task and report the evaluation results.