Unsupervised Tibetan speech features Learning based on Dynamic Bayesian Networks

This paper proposed an unsupervised learning method to learn speech features based on Dynamic Bayesian Networks (DBNs) that accounts for the spatiotemporal dependences in speech signal. Although deep networks have been successfully applied to unsupervised learning features, the structures of the deep networks are often fixed before learning and they fail to capture temporal representation. In this paper, we propose to construct DBNs for unsupervised learning spatial-temporal features from speech data. The experiment results on Tibetan speech data showed the features learned using proposed DBNs outperforms the state-of-art methods in word recognition accuracy.