Lowresource speech recognition with automatically learned sparse inverse covariance matrices

Full covariance acoustic models trained with limited training data generalize poorly to unseen test data due to a large number of free parameters. We propose to use sparse inverse covariance matrices to address this problem. Previous sparse inverse covariance methods never outperformed full covariance methods. We propose a method to automatically drive the structure of inverse covariance matrices to sparse during training. We use a new objective function by adding L1 regularization to the traditional objective function for maximum likelihood estimation. The graphic lasso method for the estimation of a sparse inverse covariance matrix is incorporated into the Expectation Maximization algorithm to learn parameters of HMM using the new objective function. Experimental results show that we only need about 25% of the parameters of the inverse covariance matrices to be nonzero in order to achieve the same performance of a full covariance system. Our proposed system using sparse inverse covariance Gaussians also significantly outperforms a system using full covariance Gaussians trained on limited data.