Weakly supervised keyword learning using sparse representations of speech.

When applied to speech, Non-negative Matrix Factorization is capable of learning a small vocabulary of words, foregoing any prior linguistic knowledge. This makes it adequate for small-scale speech applications where flexibility is of the utmost importance, e.g. assistive technology for the speech impaired. However, its performance depends on the way its inputs are represented. We propose the use of exemplar-based sparse representations of speech, and explore the influence of some of these representation's basic parameters, such as the total number of exemplars considered and the sparseness imposed on them. We show that the resulting learning performance compares favorably with those of previously proposed approaches. [ABSTRACT FROM PUBLISHER]