12 citations · 20 across the 4 of their papers we have counts for
8 papers
DeepA: A Deep Neural Analyzer For Speech And Singing Vocoding
Sergey Nikonorov, Berrak Sisman, Mingyang Zhang +1
Conventional vocoders are commonly used as analysis tools to provide interpretable features for downstream tasks such as speech synthesis and voice conversion. They are built under…
Across-Task Neural Architecture Search via Meta Learning
Jingtao Rong, Xinyi Yu, Mingyang Zhang +1
Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search(NAS). It is challenging to apply NAS in meta-learning scen…
Effective Model Compression via Stage-wise Pruning
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
Automated Machine Learning(Auto-ML) pruning methods aim at searching a pruning strategy automatically to reduce the computational complexity of deep Convolutional Neural Networks(d…
Transfer Learning from Speech Synthesis to Voice Conversion with Non-Parallel Training Data
Mingyang Zhang, Yi Zhou, Li Zhao +1
This paper presents a novel framework to build a voice conversion (VC) system by learning from a text-to-speech (TTS) synthesis system, that is called TTS-VC transfer learning. We…
Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion
Kun Zhou, Berrak Sisman, Mingyang Zhang +1
Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotio…
VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019
Andros Tjandra, Berrak Sisman, Mingyang Zhang +3
We describe our submitted system for the ZeroSpeech Challenge 2019. The current challenge theme addresses the difficulty of constructing a speech synthesizer without any text or ph…