Combination of syllable based N-gram search and word search for spoken term detection through spoken queries and IV/OOV classification

Abstract

This paper presents a Japanese spoken term detection method for spoken queries using a combination of word-based search and syllable-based N-gram search with in-vocabulary/out-of-vocabulary (IV/OOV) term classification. The N-gram index in a recognized syllable-based lattice for OOV terms, which assumes recognition errors such as substitution, insertion and deletion errors, incorporates a distance metric as a confidence score. To address spoken queries, we propose an automatic method for discriminating IV and OOV terms by using the confidence scores of spoken queries through large-vocabulary/syllable continuous speech recognition. Evaluation on an academic lecture presentation database with 44 hours of data shows that the combination of word search and syllable-based N-gram search yields significant improvement and outperforms the baseline syllable-based DTW approach.