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In this paper, we compare two paradigms for unsupervised discovery ofstructured acoustic tokens directly from speech corpora without any humanannotation. The Multigranular Paradigm seeks to capture all availableinformation in the corpora with multiple sets of tokens for different modelgranularities. The Hierarchical Paradigm attempts to jointly learn severallevels of signal representations in a hierarchical structure. The two paradigmsare unified within a theoretical framework in this paper. Query-by-ExampleSpoken Term Detection (QbE-STD) experiments on the QUESST dataset of MediaEval2015 verifies the competitiveness of the acoustic tokens. The EnhancedRelevance Score (ERS) proposed in this work improves both paradigms for thetask of QbE-STD. We also list results on the ABX evaluation task of the ZeroResource Challenge 2015 for comparison of the Paradigms.

Unsupervised Discovery of Structured Acoustic Tokens with Applications to Spoken Term Detection