Sparse modeling of posterior exemplars for keyword detection
Dhananjay Ram, Afsaneh Asaei, Pranay Dighe, Hervé A. Bourlard · 2015
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.