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 confidence 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.