Gating recurrent mixture density networks for acoustic modeling in statistical parametric speech synthesis

Wenfu Wang; Shuang Xu; Bo Xu

ICASSP 2016accepted0 citations

Gating recurrent mixture density networks for acoustic modeling in statistical parametric speech synthesis

Abstract

Though recurrent neural networks (RNNs) using long short-term memory (LSTM) units can address the issue of long-span dependencies across the linguistic inputs and have achieved the state-of-the-art performance for statistical parametric speech synthesis (SPSS), another limitation of the intrinsic uni-Gaussian nature of mean square error (MSE) objective function still remains. This paper proposes a gating recurrent mixture density network (GRMDN) architecture to jointly address these two problems in neural network based SPSS. What's more, the gated recurrent unit (GRU), which is much simpler and has more intelligible work mechanism than LSTM, is also investigated as an alternative gating unit in RNN based acoustic modeling. Experimental results show that the proposed GRMDN architecture can synthesize more natural speech than its MSE-trained counterpart and both the two gating units (LSTM and GRU) show comparable performance.

BibTeX

@inproceedings{icassp2016_gatingrecurrentm,
  title = {Gating recurrent mixture density networks for acoustic modeling in statistical parametric speech synthesis},
  author = {Wenfu Wang and Shuang Xu and Bo Xu},
  booktitle = {ICASSP 2016},
  year = {2016}
}