Stargan-vc2: Rethinking Conditional Methods For Stargan-based Voice Conversion
2019 · Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, et al.
Abstract
Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings among multiple domains without relying on parallel data. This is important but challenging owing to the requirement of learning multiple mappings and the non-availability of explicit supervision. Recently, StarGAN-VC has garnered attention owing to its ability to solve this problem only using a single generator. However, there is still a gap between real and converted speech. To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2. Particularly, we rethink conditional methods in two aspects: training objectives and network architectures. For the former, we propose a source-and-target conditional adversarial loss that allows all source domain data to be convertible to the target domain data. For the latter, we introduce a modulation-based conditional method
Authors
(none)
Tags
Stats
Related papers
- Stargan-vc: Non-parallel Many-to-many Voice Conversion With Star Generative Adversarial Networks (2018)18.09
- Starganv2-vc: A Diverse, Unsupervised, Non-parallel Framework For Natural-sounding Voice Conversion (2021)13.70
- Stargan-vc+asr: Stargan-based Non-parallel Voice Conversion Regularized By Automatic Speech Recognition (2021)5.24
- Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks (2020)9.92
- Cyclegan-vc2: Improved Cyclegan-based Non-parallel Voice Conversion (2019)17.45
- Stargan-zsvc: Towards Zero-shot Voice Conversion In Low-resource Contexts (2021)3.58
- Stargan-vc++: Towards Emotion Preserving Voice Conversion Using Deep Embeddings (2023)2.26
- Many-to-many Voice Conversion Using Conditional Cycle-consistent Adversarial Networks (2020)10.85