Laplace Group Sensing for Acoustic Models

Abstract

This paper presents the group sparse learning for acoustic models where a sequence of acoustic features is driven by Markov chain and each feature vector is represented by groups of basis vectors. The group of common bases represents the features across Markov states within a regression class. The group of individual basis compensates the intra-state residual information. Laplace distribution is used as the sparse prior of sensing weights for group basis representation. Laplace parameter serves as regularization parameter or automatic relevance determination which controls the selection of relevant bases for acoustic modeling. The groups of regularization parameters and basis vectors are estimated from training data by maximizing the marginal likelihood over sensing weights which is implemented by Laplace approximation using the Hessian matrix and the maximum a posteriori parameters. Model uncertainty is compensated through full Bayesian treatment. The connection of Laplace group sensing to lasso regularization is illustrated. Experiments on noisy speech recognition show the robustness of group sparse acoustic models in presence of different noise types and SNRs.