Multilingual exemplar-based acoustic model for the NIST Open KWS 2015 evaluation

Abstract

In this paper, we investigate the use of the proposed non-parametric exemplar-based acoustic modeling for the NIST Open Keyword Search 2015 Evaluation. Specifically, kerneldensity model is used to replace GMM in HMM/GMM (Hidden Markov Model / Gaussian Mixture Model) or DNN in HMM/DNN (Hidden Markov Model / Deep Neural Network) acoustic model to predict the emission probability of HMM states. To get further improvement, likelihood score generated by the kernel-density model is discriminatively tuned by the score tuning module realized by a neural network. Various configurations for score tuning module have been examined to show that simple neural network with 1 hidden layer is sufficient to fine tune the likelihood score generated by the kernel-density model. With this architecture, our exemplar-based model outperforms the 9-layer-DNN acoustic model significantly for both the speech recognition and keyword search tasks. In addition, our proposed exemplarbased system provides complementary information to other systems and we can further benefit from system combination.