120 citations · 414 across the 61 of their papers we have counts for
19 papers · 1 filter
Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training
Haohan Guo, Heng Lu, Na Hu +5
This paper describes an end-to-end adversarial singing voice conversion (EA-SVC) approach. It can directly generate arbitrary singing waveform by given phonetic posteriorgram (PPG)…
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Zhuohuang Zhang, Yong Xu, Meng Yu +4
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are har…
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning
Wei Xia, Chunlei Zhang, Chao Weng +2
In this study, we investigate self-supervised representation learning for speaker verification (SV). First, we examine a simple contrastive learning approach (SimCLR) with a moment…
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation
Zhaoheng Ni, Yong Xu, Meng Yu +4
This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recogni…
Improving RNN Transducer With Target Speaker Extraction and Neural Uncertainty Estimation
Jiatong Shi, Chunlei Zhang, Chao Weng +3
Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers. This work presents a joint framewo…
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech
Jianwei Yu, Shi-Xiong Zhang, Bo Wu +6
Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging tas…
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
Aswin Shanmugam Subramanian, Chao Weng, Shinji Watanabe +4
This paper proposes a new paradigm for handling far-field multi-speaker data in an end-to-end neural network manner, called directional automatic speech recognition (D-ASR), which…
Replay and Synthetic Speech Detection with Res2net Architecture
Xu Li, Na Li, Chao Weng +4
Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-cal…
Multi-Channel Speaker Verification for Single and Multi-talker Speech
Saurabh Kataria, Shi-Xiong Zhang, Dong Yu
To improve speaker verification in real scenarios with interference speakers, noise, and reverberation, we propose to bring together advancements made in multi-channel speech featu…
Peking Opera Synthesis via Duration Informed Attention Network
Yusong Wu, Shengchen Li, Chengzhu Yu +4
Peking Opera has been the most dominant form of Chinese performing art since around 200 years ago. A Peking Opera singer usually exhibits a very strong personal style via introduci…
DurIAN-SC: Duration Informed Attention Network based Singing Voice Conversion System
Liqiang Zhang, Chengzhu Yu, Heng Lu +6
Singing voice conversion is converting the timbre in the source singing to the target speaker's voice while keeping singing content the same. However, singing data for target speak…
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition
Liqiang He, Dan Su, Dong Yu
In this paper, we explore the neural architecture search (NAS) for automatic speech recognition (ASR) systems. With reference to the previous works in the computer vision field, th…
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Daniel Michelsanti, Zheng-Hua Tan, Shi-Xiong Zhang +4
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generat…
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Zhuohuang Zhang, Yong Xu, Meng Yu +3
Speech separation algorithms are often used to separate the target speech from other interfering sources. However, purely neural network based speech separation systems often cause…
Distortionless Multi-Channel Target Speech Enhancement for Overlapped Speech Recognition
Bo Wu, Meng Yu, Lianwu Chen +4
Speech enhancement techniques based on deep learning have brought significant improvement on speech quality and intelligibility. Nevertheless, a large gain in speech quality measur…
Speaker Independent and Multilingual/Mixlingual Speech-Driven Talking Head Generation Using Phonetic Posteriorgrams
Huirong Huang, Zhiyong Wu, Shiyin Kang +9
Generating 3D speech-driven talking head has received more and more attention in recent years. Recent approaches mainly have following limitations: 1) most speaker-independent meth…
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification
Xu Li, Na Li, Jinghua Zhong +5
Recently adversarial attacks on automatic speaker verification (ASV) systems attracted widespread attention as they pose severe threats to ASV systems. However, methods to defend a…
End-to-End Multi-Look Keyword Spotting
Meng Yu, Xuan Ji, Bo Wu +2
The performance of keyword spotting (KWS), measured in false alarms and false rejects, degrades significantly under the far field and noisy conditions. In this paper, we propose a…
Transferring Source Style in Non-Parallel Voice Conversion
Songxiang Liu, Yuewen Cao, Shiyin Kang +5
Voice conversion (VC) techniques aim to modify speaker identity of an utterance while preserving the underlying linguistic information. Most VC approaches ignore modeling of the sp…