In this chapter, we describe one of the several possible ways of exploiting deep neural networks (DNNs) in automatic speech recognition systems—the deep neural network-hidden Markov model (DNN-HMM) hybrid system. The DNN-HMM hybrid system takes advantage of DNN’s strong representation learning power and HMM’s sequential modeling ability, and outperforms conventional Gaussian mixture model (GMM)-HMM systems significantly on many large vocabulary continuous speech recognition tasks. We describe the architecture and the training procedure of the DNN-HMM hybrid system and point out the key components of such systems by comparing a range of system setups.
CITATION STYLE
Yu, D., & Deng, L. (2015). Deep Neural Network-Hidden Markov Model Hybrid Systems (pp. 99–116). https://doi.org/10.1007/978-1-4471-5779-3_6
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