1 research outputs found
Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit
This paper presents a deep Gaussian process (DGP) model with a recurrent
architecture for speech sequence modeling. DGP is a Bayesian deep model that
can be trained effectively with the consideration of model complexity and is a
kernel regression model that can have high expressibility. In the previous
studies, it was shown that the DGP-based speech synthesis outperformed neural
network-based one, in which both models used a feed-forward architecture. To
improve the naturalness of synthetic speech, in this paper, we show that DGP
can be applied to utterance-level modeling using recurrent architecture models.
We adopt a simple recurrent unit (SRU) for the proposed model to achieve a
recurrent architecture, in which we can execute fast speech parameter
generation by using the high parallelization nature of SRU. The objective and
subjective evaluation results show that the proposed SRU-DGP-based speech
synthesis outperforms not only feed-forward DGP but also automatically tuned
SRU- and long short-term memory (LSTM)-based neural networks.Comment: 5 pages. Accepted by ICASSP202