An hierarchical exemplar-based sparse model of speech, with an application to ASR

We propose a hierarchical exemplar-based model of speech, as well as a new algorithm, to efficiently find sparse linear combinations of exemplars in dictionaries containing hundreds of thousands exemplars. We use a variant of hierarchical agglomerative clustering to find a hierarchy connecting all exemplars, so that each exemplar is a parent to two child nodes. We use a modified version of a multiplicative-updates based algorithm to find sparse representations starting from a small active set of exemplars from the dictionary. Namely, on each iteration we replace exemplars that have an increasing weight by their child-nodes. We illustrate the properties of the proposed method by investigating computational effort, accuracy of the eventual sparse representation and speech recognition accuracy on a digit recognition task.