Experimental Evaluation of Probabilistic Similarity for Spoken Term Detection

In this paper, the use of probabilistic similarity and the likelihood ratio for spoken term detection is investigated. The object of spoken term detection is to rank retrieved spoken terms according to their distance from a query. First, we evaluate several probabilistic similarity functions for use as a sophisticated distance. In particular, we investigate probabilistic similarity for Gaussian mixture models using the closedform solutions and pseudo-sampling approximation of Kullback–Leibler divergence. And then we propose additive scoring factors based on the likelihood ratio of each individual subword. An experimental evaluation demonstrates that we can achieve an improved detection performance by using probabilistic similarity functions and applying the likelihood ratio.