  Acoustic models based on long short-term memory recurrent neural networks
(LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and
showed significant improvements in naturalness and latency over those based on
hidden Markov models (HMMs). This paper describes further optimizations of
LSTM-RNN-based SPSS for deployment on mobile devices; weight quantization,
multi-frame inference, and robust inference using an {\epsilon}-contaminated
Gaussian loss function. Experimental results in subjective listening tests show
that these optimizations can make LSTM-RNN-based SPSS comparable to HMM-based
SPSS in runtime speed while maintaining naturalness. Evaluations between
LSTM-RNN- based SPSS and HMM-driven unit selection speech synthesis are also
presented.
