An efficient lattice-based phonetic search method for accelerating keyword spotting in large speech databases

Abstract

This paper describes an algorithm for the reduction of computational complexity in phonetic search KeyWord Spotting (KWS). This reduction is particularly important when searching for keywords within very large speech databases and aiming for rapid response time. The suggested algorithm consists of an anchor-based phoneme search that reduces the search space by generating hypotheses only around phonemes recognized with high reliability. Three databases have been used for the evaluation: IBM Voicemail I and Voicemail II, consisting of long spontaneous utterances and the Wall Street Journal portion of the MACROPHONE database, consisting of read speech utterances. The results indicated a significant reduction of nearly 90 % in the computational complexity of the search while improving the false alarm rate, with only a small decrease in the detection rate in both databases. Search space reduction, as well as, performance gain or loss can be controlled according to the user preferences via the suggested algorithm parameters and thresholds.