Embedding time warping in exemplar-based sparse representations of speech

This paper describes a new sparse representation model for speech that allows time warping as an extension to a recently proposed sparse representations-based speech recognition system. This recognition system uses exemplars to model the acoustics which are labeled speech occurrences of different length extracted from the training data. Exemplars are organized in multiple dictionaries on the basis of their class and length. Input speech segments are approximated as a sparse linear combination of the exemplars using these dictionaries and a reconstruction error-based decoding is adopted in order to find the best matching class sequence. With the current sparse representation model using a dictionary and a weight vector to approximate an input speech segment, it is not possible to compare input speech segments with exemplars of different lengths. The goal of this work is to introduce a novel sparse representation model which allows time warping using a third matrix which linearly combines consecutive frames in order to shrink or expand the approximation. Preliminary results have shown the feasibility of the proposed sparse representation model.