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We propose two simple methods to improve the performance of a keyword spotting system. In our application, the users are allowed to change the keywords anytime if they want. Thus we focused on phone-based GMM-HMM models since they do not require keyword-specific training data. However, the GMM-HMM based models usually have very high false alarm rate, i.e., a keyword is not present but the system gives...
Training very deep neural networks is very difficult because of gradient degradation. However, the incomparable expressiveness of the many deep layers is highly desirable at testing time and usually leads to better performance. Recently, training techniques such as residual networks that enable us to train very deep networks have proved to be a great success. In this paper, we studied the application...
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...
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...
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