Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices
Table 2: Subjective preference scores (%) between LSTM-RNNs using 4-frame bundled inference with data augmentation (4-frame) and single-frame inference (1-frame).
['Language', 'CG', 'L2', 'No pref.']
It can be seen from the table that the effect of quantization was negligible. The disk footprint of the acoustic LSTM-RNN for English (NA) was reduced from 1.05 MBytes to 272 KBytes. Note that weights of the LSTM-RNNs were quantized to 8-bit integers. It can be seen from the table that LSTM-RNN with multi-frame inference with data augmentation achieved the same naturalness as that with single-frame one. Compared with 1-frame, 4-frame achieved about 40% reduction of walltime at runtime synthesis.