2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-50.4 | ||
| Paper Title | SEMI-SUPERVISED LEARNING FOR SINGING SYNTHESIS TIMBRE | ||
| Authors | Jordi Bonada, Merlijn Blaauw, Universitat Pompeu Fabra, Spain | ||
| Session | SPE-50: Voice Conversion & Speech Synthesis: Singing Voice & Other Topics | ||
| Location | Gather.Town | ||
| Session Time: | Friday, 11 June, 11:30 - 12:15 | ||
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-SYNT] Speech Synthesis and Generation | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | We propose a semi-supervised singing synthesizer, which is able to learn new voices from audio data only, without any annotations such as phonetic segmentation. Our system is an encoder-decoder model with two encoders, linguistic and acoustic, and one (acoustic) decoder. In a first step, the system is trained in a supervised manner, using a labeled multi-singer dataset. Here, we ensure that the embeddings produced by both encoders are similar, so that we can later use the model with either acoustic or linguistic input features. To learn a new voice in an unsupervised manner, the pretrained acoustic encoder is used to train a decoder for the target singer. Finally, at inference, the pretrained linguistic encoder is used together with the decoder of the new voice, to produce acoustic features from linguistic input. We evaluate our system with a listening test and show that the results are comparable to those obtained with an equivalent supervised approach. | ||