Sparse Banded Precision Matrices for Low Resource Speech Recognition

We propose to use sparse banded precision matrices for speech recognition when there is insufficient training data. Previously we proposed a method to drive the structure of precision matrices to sparse under the HMM framework during training. The recognition accuracy of this compact model is shown to be better than full covariance or diagonal covariance systems. In this paper we propose to modify the penalization in order to automatically learn sparse banded precision matrices. This will enable the trained models to be even more compact. We demonstrate that the feature order is critical to the success of our proposed method. Using our proposed feature order, we can substantially reduce the right half-bandwidth of the banded precision matrices without sacrificing the recognition accuracy. This saves both memory and computation.