120 citations · 414 across the 61 of their papers we have counts for
13 papers · 1 filter
Towards Unified All-Neural Beamforming for Time and Frequency Domain Speech Separation
Rongzhi Gu, Shi-Xiong Zhang, Yuexian Zou +1
Recently, frequency domain all-neural beamforming methods have achieved remarkable progress for multichannel speech separation. In parallel, the integration of time domain network…
Deep Neural Mel-Subband Beamformer for In-car Speech Separation
Vinay Kothapally, Yong Xu, Meng Yu +2
While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies indep…
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification
Chunlei Zhang, Dong Yu
Self-supervised learning (SSL) has drawn an increased attention in the field of speech processing. Recent studies have demonstrated that contrastive learning is able to learn discr…
Towards Improved Zero-shot Voice Conversion with Conditional DSVAE
Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli +1
Disentangling content and speaking style information is essential for zero-shot non-parallel voice conversion (VC). Our previous study investigated a novel framework with disentang…
LAE: Language-Aware Encoder for Monolingual and Multilingual ASR
Jinchuan Tian, Jianwei Yu, Chunlei Zhang +3
Despite the rapid progress in automatic speech recognition (ASR) research, recognizing multilingual speech using a unified ASR system remains highly challenging. Previous works on…
Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE
Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli +1
In this paper, we propose a novel unsupervised text-to-speech acoustic model training scheme, named UTTS, which does not require text-audio pairs. UTTS is a multi-speaker speech sy…
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Meng Yu, Yong Xu, Chunlei Zhang +2
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when…
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Rongjie Huang, Max W. Y. Lam, Jun Wang +4
Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinde…
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition
Zhao You, Shulin Feng, Dan Su +1
Recently, Conformer based CTC/AED model has become a mainstream architecture for ASR. In this paper, based on our prior work, we identify and integrate several approaches to achiev…
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion
Jiachen Lian, Chunlei Zhang, Dong Yu
Traditional studies on voice conversion (VC) have made progress with parallel training data and known speakers. Good voice conversion quality is obtained by exploring better alignm…
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Max W. Y. Lam, Jun Wang, Dan Su +1
Diffusion probabilistic models (DPMs) and their extensions have emerged as competitive generative models yet confront challenges of efficient sampling. We propose a new bilateral d…
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion
Disong Wang, Shan Yang, Dan Su +3
Though significant progress has been made for speaker-dependent Video-to-Speech (VTS) synthesis, little attention is devoted to multi-speaker VTS that can map silent video to speec…
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs
Songxiang Liu, Dan Su, Dong Yu
Denoising diffusion probabilistic models (DDPMs) are expressive generative models that have been used to solve a variety of speech synthesis problems. However, because of their hig…