Wordspotting using a predictive neural model for the telephone speech corpus

We describe a wordspotting algorithm based on a predictive neural model for a telephone speech corpus. Each keyword is modeled as a whole word. For keyword detection scoring we used a minimum accumulated prediction residual. We computed empirically a threshold value for rejecting non-keyword speech in place of building non-keyword models. We tested the algorithm with the TUBTEL telephone speech corpus and compared it with other algorithms like the standard DTW-based wordspotting algorithm and the two-stage wordspotting algorithm based on a DTW and a multilayer perceptron