Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices
Table 5: Subjective preference scores (%) between the LSTM-RNN- and HMM-based SPSS systems .
['Length', 'Latency (ms) LSTM', 'Latency (ms) HMM', 'Total (ms) LSTM', 'Total (ms) HMM']
The average disk footprints of HMMs and LSTM-RNNs including both acoustic and duration models over 6 languages were 1560 and 454.5 KBytes, respectively. It can be seen from the table that the LSTM-RNN-based system could synthesize speech with lower latency and total synthesis time than the HMM-based system. However, it is worthy noting that the LSTM-RNN-based system was 15–22% slower than the HMM-based system in terms of the total synthesis time on old devices having ARM CPUs without the NEON instruction set (latency was still lower). It shows that the LSTM-RNN-based system could synthesize more naturally sounding synthesized speech than the HMM-based one.