13 citations · 58 across the 17 of their papers we have counts for
17 papers
Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates
Guojun Xu, Mingyang Zhang, Jianwen Xiang +3
Distributed Image Compression (DIC) is crucial for multi-view transmission, especially when operating at extremely low bitrates (< 0.1 bpp). Its core challenge is effectively utili…
RefXVC: Cross-Lingual Voice Conversion with Enhanced Reference Leveraging
Mingyang Zhang, Yi Zhou, Yi Ren +3
This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that leverages reference information to improve conversion performance. Previous XVC works generally t…
Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesis
Xuehao Zhou, Mingyang Zhang, Yi Zhou +2
Generating speech across different accents while preserving speaker identity is crucial for various real-world applications. However, accurately and independently modeling both spe…
High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units
Junchen Lu, Berrak Sisman, Mingyang Zhang +1
The goal of Automatic Voice Over (AVO) is to generate speech in sync with a silent video given its text script. Recent AVO frameworks built upon text-to-speech synthesis (TTS) have…
Accented Text-to-Speech Synthesis with Limited Data
Xuehao Zhou, Mingyang Zhang, Yi Zhou +2
This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference)…
TTS-Guided Training for Accent Conversion Without Parallel Data
Yi Zhou, Zhizheng Wu, Mingyang Zhang +2
Accent Conversion (AC) seeks to change the accent of speech from one (source) to another (target) while preserving the speech content and speaker identity. However, many AC approac…
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…
VisualTTS: TTS with Accurate Lip-Speech Synchronization for Automatic Voice Over
Junchen Lu, Berrak Sisman, Rui Liu +2
In this paper, we formulate a novel task to synthesize speech in sync with a silent pre-recorded video, denoted as automatic voice over (AVO). Unlike traditional speech synthesis,…
RepNAS: Searching for Efficient Re-parameterizing Blocks
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
In the past years, significant improvements in the field of neural architecture search(NAS) have been made. However, it is still challenging to search for efficient networks due to…
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…
Graph Pruning for Model Compression
Mingyang Zhang, Xinyi Yu, Jingtao Rong +1
Previous AutoML pruning works utilized individual layer features to automatically prune filters. We analyze the correlation for two layers from the different blocks which have a sh…
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…
Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet
Mingyang Zhang, Xin Wang, Fuming Fang +2
We investigated the training of a shared model for both text-to-speech (TTS) and voice conversion (VC) tasks. We propose using an extended model architecture of Tacotron, that is a…
Error Reduction Network for DBLSTM-based Voice Conversion
Mingyang Zhang, Berrak Sisman, Sai Sirisha Rallabandi +2
So far, many of the deep learning approaches for voice conversion produce good quality speech by using a large amount of training data. This paper presents a Deep Bidirectional Lon…