Sparse modeling of posterior exemplars for keyword detection

Sparse representation has been shown to be a powerful modeling framework for classification and detection tasks. In this paper, we propose a new keyword detection algorithm based on sparse representation of the posterior exemplars. The posterior exemplars are phone conditional probabilities obtained from a deep neural network. This method relies on the concept that a keyword exemplar lies in a low-dimensional subspace which can be represented as a sparse linear combination of the training exemplars. The training exemplars are used to learn a dictionary for sparse representation of the keywords and background classes. Given this dictionary, the sparse representation of a test exemplar is used to detect the keywords. The experimental results demonstrate the potential of the proposed sparse modeling approach and it compares favorably with the state-of-the-art HMM-based framework on Numbers’95 database.