AN INNER-PRODUCT LOWER-BOUND ESTIMATE FOR DYNAMIC TIME WARPING

In this paper, we present a lower-bound estimate for dynamic time warping (DTW) on time series consisting of multi-dimensional posterior probability vectors known as posteriorgrams. We develop a lower-bound estimate based on the inner-product distance that has been found to be an effective metric for computing similarities between posteriorgrams. In addition to deriving the lower-bound estimate, we show how it can be efficiently used in an admissible K nearest neighbor (KNN) search for spotting matching sequences. We quantify the amount of computational savings achieved by performing a set of unsupervised spoken keyword spotting experiments using Gaussian mixture model posteriorgrams. In these experiments the proposed lower-bound estimate eliminates 89% of the DTW previously required calculations without affecting overall keyword detection performance.