Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices
Table 6: Subjective preference scores (%) between the LSTM-RNN-based SPSS and HMM-driven unit selection TTS (Hybrid) systems. Note that “Spanish (NA)” and “Portuguese (BR)” indicate North American Spanish and Brazilian Portuguese, respectively.
['Language', 'LSTM', 'Hybrid', 'No pref.']
Both TTS systems used the same training data and text processing front-end modules. Note that additional linguistic features which were only available with the server-side text processing front-end modules were used in both systems. The HMM-driven unit selection TTS systems were built from speech at 16 kHz sampling. Although LSTM-RNNs were trained from speech at 22.05 kHz sampling, speech at 16 kHz sampling was synthesized at runtime using a resampling functionality in Vocaine These LSTM-RNNs had the same network architecture as the one described in the previous section. They were trained with the ϵ-contaminated Gaussian loss function and utilized 4-frame bundled inference. It can be seen from the table that the LSTM-RNN-based SPSS systems were preferred to the HMM-driven unit selection TTS systems in 10 of 26 languages, while there was no significant preference between them in 3 languages. Note that the LSTM-RNN-based SPSS systems were 3–10% slower but 1,500–3,500 times smaller in disk footprint than the hybrid ones.