Name
Abstract | ||
|---|---|---|
With the development of deep learning, end-to-end neural text-to-speech (TTS) systems have achieved significant improvements in high-quality speech synthesis. However, most of these systems are attention-based autoregressive models, resulting in slow synthesis speed and large model parameters. In addition, speech in different languages is usually synthesized using different models, which increases the complexity of the speech synthesis systems. In this paper, we propose a new lightweight multi-speaker multi-lingual speech synthesis system, named LightTTS, which can quickly synthesize the Chinese, English or code-switch speech of multiple speakers in a non-autoregressive generation manner using only one model. Moreover, compared to FastSpeech with the same number of neural network layers and nodes, our LightTTS achieves a 2.50x Mel-spectrum generation acceleration on CPU, and the parameters are compressed by 12.83x. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414400 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Multi-speaker, multi-lingual, speech synthesis, non-autoregressive, lightweight | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Li | 1 | 0 | 1.69 |
| Beibei Ouyang | 2 | 0 | 0.34 |
| Lin Li | 3 | 12 | 4.60 |
| Q. Y. Hong | 4 | 50 | 15.79 |