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Pre-filtered dynamic time warping for posteriorgram based keyword search

In this study, we present a pre-filtering method for dynamic time warping (DTW) to improve the efficiency of a posteriorgram based keyword search (KWS) system. The ultimate aim is to improve the performance of a large vocabulary continuous speech recognition (LVCSR) based KWS system using the posteriorgram based KWS approach. We use phonetic posteriorgrams to represent the audio data and generate average posteriorgrams to represent the given text queries. The DTW algorithm is used to determine the optimal alignment between the posteriorgrams of the audio data and the queries. Since DTW has quadratic complexity, it can be relatively inefficient for keyword search. Our main contribution is to reduce this complexity by pre-filtering based on a vector space representation of the two posteriorgrams without any degradation in performance. Experimental results show that our system reduces the complexity and when combined with the baseline LVCSR based KWS system, it improves the performance both for the out-of-vocabulary (OOV) queries and the in-vocabulary (IV) queries.