Editing Hidden Markov Models in Speech Synthesis (section)

==== Integration of Deep Neural Network and HMM (DNN-HMM) ====
# DNN for Acoustic Modeling: Employing deep neural networks to model HMM state emission probability, replacing the traditional Gaussian mixture model (GMM), significantly enhancing the accuracy and naturalness of the speech synthesis system. [Hinton et al., 2012]<ref name=":0" />
# DNN as a Front-end Feature Extractor: Utilizing DNN as a feature extractor and its output as the input feature of HMM to enhance feature representation. [Zeiler et al., 2013]<ref>Zeiler, M. D., Krishnan, D., Taylor, G. W., Fergus, R. (2013). [https://arxiv.org/abs/1310.1531 Deconvolutional networks. In Computer Vision and Pattern Recognition] (CVPR), 2010 IEEE Conference on (pp. 2528-2535).</ref>