In this paper we introduce a novel dynamic programming algorithm called Information Retrieval-based Dynamic Time Warping (IR-DTW) used to find non-linearly matching subsequences between two time series where matching start and end points are not known a priori. In this paper our algorithm is applied for audio matching within the query by example (QbE) spoken term detection (STD) task, although it is applicable to many other problems. The main advantages of the proposed algorithm in comparison to similar approaches are twofold. On the one hand, IR-DTW requires a much smaller memory footprint than standard Dynamic Time Warping (DTW) approaches. On the other hand, it allows for the application of indexing techniques to the search collection for increased matching speed, which makes IR-DTW suitable for application in large scale implementations. We show through preliminary experimentation with a QbE-STD task that the memory footprint is greatly reduced in comparison to a baseline subsequence-DTW (S-DTW) implementation and that its matching accuracy is much better than that of pure diagonal matching and just slightly worse than that of S-DTW.
Bibliographic reference. Anguera, Xavier (2013): "Information retrieval-based dynamic time warping", In INTERSPEECH-2013, 1-5.