Using Multiple Speech Recognition Results to Enhance STD with Suffix Array on the NTCIR-10 SpokenDoc-2 Task

We have previously proposed a fast spoken term detection method that uses a suffix array as a data structure. By applying dynamic time warping on a suffix array, we achieved very quick keyword detection from a very large-scale speech document. In this study, we modify our method so that it can deal with multiple recognition results. By using these results obtained from various speech recognizers, search performance will improve as a consequence of the complementary effect of using different language and acoustic models. Experimental results show the maximum value of F-measure and the MAP score increased by 6% to 10%.