Speech and Vision Lab

This paper describes the experiments conducted for spoken web search (SWS) at MediaEval 2013 evaluations. A conventional approach is to train a multi-layer perceptron using high resource languages and then use it in the low resource scenario. However, phone posteriorgrams have been found to under-perform when the language they were trained on differs from the target language. In this paper, we use bottle-neck features derived from MLP to generate Gaussian posteriorgrams. We also use a variant of dynamic time warping (DTW) based technique which exploits the redundancy in speech signal and thus averages the successive Gaussian posteriorgrams to reduce the length of the spoken query and spoken reference.