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This paper presents a deep Gaussian process (DGP) model with a recurrentarchitecture for speech sequence modeling. DGP is a Bayesian deep model thatcan be trained effectively with the consideration of model complexity and is akernel regression model that can have high expressibility. In the previousstudies, it was shown that the DGP-based speech synthesis outperformed neuralnetwork-based one, in which both models used a feed-forward architecture. Toimprove the naturalness of synthetic speech, in this paper, we show that DGPcan be applied to utterance-level modeling using recurrent architecture models.We adopt a simple recurrent unit (SRU) for the proposed model to achieve arecurrent architecture, in which we can execute fast speech parametergeneration by using the high parallelization nature of SRU. The objective andsubjective evaluation results show that the proposed SRU-DGP-based speechsynthesis outperforms not only feed-forward DGP but also automatically tunedSRU- and long short-term memory (LSTM)-based neural networks.

Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit