3 papers
eess.AS2018
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder
Yi Zhao, Shinji Takaki, Hieu-Thi Luong +3
Recent neural networks such as WaveNet and sampleRNN that learn directly from speech waveform samples have achieved very high-quality synthetic speech in terms of both naturalness…
eess.AS2018
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda +4
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different stat…
eess.AS2018
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
Tomi Kinnunen, Jaime Lorenzo-Trueba, Junichi Yamagishi +4
Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve…