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This paper presents a new approach for unsupervised Spoken Term Detectionwith spoken queries using multiple sets of acoustic patterns automaticallydiscovered from the target corpus. The different pattern HMMconfigurations(number of states per model, number of distinct models, number ofGaussians per state)form a three-dimensional model granularity space. Differentsets of acoustic patterns automatically discovered on different points properlydistributed over this three-dimensional space are complementary to one another,thus can jointly capture the characteristics of the spoken terms. Byrepresenting the spoken content and spoken query as sequences of acousticpatterns, a series of approaches for matching the pattern index sequences whileconsidering the signal variations are developed. In this way, not only theon-line computation load can be reduced, but the signal distributions caused bydifferent speakers and acoustic conditions can be reasonably taken care of. Theresults indicate that this approach significantly outperformed the unsupervisedfeature-based DTW baseline by 16.16\% in mean average precision on the TIMITcorpus.

Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity