| Paper ID | SPE-12.1 |
| Paper Title |
TOWARDS LOW-RESOURCE STARGAN VOICE CONVERSION USING WEIGHT ADAPTIVE INSTANCE NORMALIZATION |
| Authors |
Mingjie Chen, Yanpei Shi, Thomas Hain, University of Sheffield, United Kingdom |
| Session | SPE-12: Voice Conversion 2: Low-Resource & Cross-Lingual Conversion |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-SYNT] Speech Synthesis and Generation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Many-to-many voice conversion with non-parallel training data has seen significant progress in recent years. It is challenging because of lacking of ground truth parallel data. StarGAN-based models have gained attentions because of their efficiency and effectiveness. However, most of the StarGAN-based works only focused on small number of speakers and large amount of training data. In this work, we aim at improving the data efficiency of the model and achieving a many-to-many non-parallel StarGAN-based voice conversion for a relatively large number of speakers with limited training samples. In order to improve data efficiency, the proposed model uses a speaker encoder for extracting speaker embeddings and weight adaptive instance normalization (W-AdaIN) layers. Experiments are conducted with 109 speakers under two low-resource situations, where the number of training samples is 20 and 5 per speaker. An objective evaluation shows the proposed model outperforms baseline methods significantly. Furthermore, a subjective evaluation shows that, for both naturalness and similarity, the proposed model outperforms baseline method. |
