Building text-to-speech (TTS) systems requires large amounts of high quality speech recordings and annotations, which is a challenge to collect especially considering the variation in spoken languages around the world. Acoustic modeling techniques that could utilize inhomogeneous data are hence important as they allow us to pool more data for training. This paper presents a long short-term memory (LSTM) recurrent neural network (RNN) based statistical parametric speech synthesis system that uses data from multiple languages and speakers. It models language variation through cluster adaptive training and speaker variation with speaker dependent output layers. Experimental results have shown that the proposed multilingual TTS system can synthesize speech in multiple languages from a single model while maintaining naturalness. Furthermore, it can be adapted to new languages with only a small amount of data.
Cite as: Li, B., Zen, H. (2016) Multi-Language Multi-Speaker Acoustic Modeling for LSTM-RNN Based Statistical Parametric Speech Synthesis. Proc. Interspeech 2016, 2468-2472, doi: 10.21437/Interspeech.2016-172
@inproceedings{li16g_interspeech,
author={Bo Li and Heiga Zen},
title={{Multi-Language Multi-Speaker Acoustic Modeling for LSTM-RNN Based Statistical Parametric Speech Synthesis}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={2468--2472},
doi={10.21437/Interspeech.2016-172}
}