Mar 18, 2016 · Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts.
ABSTRACT. We study large-scale kernel methods for acoustic modeling and com- pare to DNNs on performance metrics related to both acoustic mod-.
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
A new technique, entropy regularized perplexity, is proposed, which can noticeably improve the recognition performance of both types of models, and reduces ...
Mar 18, 2016 · ABSTRACT. We study large-scale kernel methods for acoustic modeling and com- pare to DNNs on performance metrics related to both acoustic ...
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How to scale up kernel methods to be as good as deep neural nets ... A comparison between deep neural nets and kernel acoustic models for speech recognition.
We study the performance of kernel methods on the acoustic modeling task for automatic speech recognition, and compare their performance to deep neural networks ...
May 21, 2015 · Abstract. Deep Neural Networks (DNNs) are effective models for machine learning. Unfortunately, training a DNN is extremely time-consuming, ...
Apr 1, 2019 · We study the performance of kernel methods on the acoustic modeling task for automatic speech recognition, and compare their performance to deep ...
DNN-HMMs outperform conventional GMM-HMMs by a large margin for all spoken tasks commonly used in spoken assessment applications. In our experiments, DNN-HMMs ...