15 citations · 15 across the 6 of their papers we have counts for
6 papers
VNDUQE: Information-Theoretic Novelty Detection using Deep Variational Information Bottleneck
Aryan Gondkar, Hayder Radha, Yiming Deng
Detecting out-of-distribution (OOD) samples is critical for safe deployment of neural networks in safety-critical applications. While maximum softmax probability (MSP) provides a s…
Learning Expressive Disentangled Speech Representations with Soft Speech Units and Adversarial Style Augmentation
Yimin Deng, Jianzong Wang, Xulong Zhang +2
Voice conversion is the task to transform voice characteristics of source speech while preserving content information. Nowadays, self-supervised representation learning models are…
Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion refers to transferring speaker identity with well-preserved content. Better disentanglement of speech representations leads to better voice conversion. Recent stud…
CP-EB: Talking Face Generation with Controllable Pose and Eye Blinking Embedding
Jianzong Wang, Yimin Deng, Ziqi Liang +3
This paper proposes a talking face generation method named "CP-EB" that takes an audio signal as input and a person image as reference, to synthesize a photo-realistic people talki…
CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
Yimin Deng, Xulong Zhang, Jianzong Wang +2
Better disentanglement of speech representation is essential to improve the quality of voice conversion. Recently contrastive learning is applied to voice conversion successfully b…
PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion as the style transfer task applied to speech, refers to converting one person's speech into a new speech that sounds like another person's. Up to now, there has be…