Wall Street Journal Data Research Articles

Efficient Sparse Banded Acoustic Models for Speech Recognition

We propose sparse banded acoustic models to significantly improve the recognition accuracy and reduce the computational cost of speech recognition systems. The sparse banded models are trained using a weighted lasso regularization. In addition, we propose new feature orders to reduce the bandwidth of sparse banded models in order to speed up computation. Experimental results on the Wall Street Journal data set show that sparse banded models significantly outperform diagonal and full covariance models by 9.5% and 15.1% relatively. Sparse banded models also run the fastest. The advantages of sparse banded models are also demonstrated on the collected Cantonese data set.

Sparse Inverse Covariance Matrices for Low Resource Speech Recognition

We propose to use sparse inverse covariance matrices for acoustic model training when there is insufficient training data. Acoustic models trained with inadequate training data tend to over fit, generalizing poorly to unseen test data, especially when full covariance matrices are used. We address this problem by adding an L1 regularization term to the traditional objective function for maximum likelihood estimation, to penalize complex models. The structure of the inverse covariance matrices will be automatically sparsified using this new objective function. The Expectation Maximization algorithm is used to learn the parameters of the hidden Markov model using the new objective function. It is shown that the training procedures for all the hidden Markov model parameters are the same as that of maximum likelihood estimation except the inverse covariance matrices. The update equation for the inverse covariance matrices is concave and can be solved efficiently. Our experiments show that this proposed method can correctly learn the underlying correlations among the random variables of the speech feature vector. Experimental results on the Wall Street Journal data show that our proposed model significantly outperforms the diagonal covariance model and the full covariance model by 10.9% and 16.5% relative recognition accuracy, when only about 14 hours of training data are available. On our collected low resource language data-the Cantonese data set, the proposed model also significantly outperforms the diagonal covariance model and the full covariance model.

Towards Robust Semantic Role Labeling

Most semantic role labeling (SRL) research has been focused on training and evaluating on the same corpus. This strategy, although appropriate for initiating research, can lead to overtraining to the particular corpus. This article describes the operation of assert, a state-of-the art SRL system, and analyzes the robustness of the system when trained on one genre of data and used to label a different genre. As a starting point, results are first presented for training and testing the system on the PropBank corpus, which is annotated Wall Street Journal (WSJ) data. Experiments are then presented to evaluate the portability of the system to another source of data. These experiments are based on comparisons of performance using PropBanked WSJ data and PropBanked Brown Corpus data. The results indicate that whereas syntactic parses and argument identification transfer relatively well to a new corpus, argument classification does not. An analysis of the reasons for this is presented and these generally point to the nature of the more lexical/semantic features dominating the classification task where more general structural features are dominant in the argument identification task.