Zero-resource spoken term detection using hierarchical graph-based similarity search

This paper presents fast zero-resource spoken term detection (STD) in a large-scale data set, by using a hierarchical graph-based similarity search method (HGSS). HGSS is an improved graph-based similarity search method (GSS) in terms of a search space for high-speed performance. Instead of a degree-reduced k-nearest neighbor (k-DR) graph for GSS, a hierarchical k-DR graph, which is constructed based on a cluster structure in the corresponding k-DR graph, is used as an index for HGSS. A search algorithm for the hierarchical k-DR graph effectively utilizes the cluster structure, resulting in the reduction of the search space. HGSS inherits the useful property of GSS; it is available for any data sets without limits on a data type nor a defined dissimilarity since a graph is a general expression of a relationship between objects. A vertex and an edge in the hierarchical graph correspond to a Gaussian mixture model (GMM) posterior-gram segment and the relationship between a pair of GMM poste-riorgram segments, which is measured by dynamic time warping, respectively. Experimental results demonstrate that HGSS successfully reduces the computational cost by more than 40 % at nearly the same accuracy, compared to GSS.