24 citations · 90 across the 19 of their papers we have counts for
19 papers
Make-A-Voice: Unified Voice Synthesis With Discrete Representation
Rongjie Huang, Chunlei Zhang, Yongqi Wang +7
Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common. In addition, the majority of voice synthe…
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…
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…
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
Soumi Maiti, Yushi Ueda, Shinji Watanabe +4
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting. Our proposed framework integrates speak…
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Brian Yan, Chunlei Zhang, Meng Yu +6
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a genera…
MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment
Meng Yu, Chunlei Zhang, Yong Xu +2
The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech q…
Towards Robust Speaker Verification with Target Speaker Enhancement
Chunlei Zhang, Meng Yu, Chao Weng +1
This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embeddin…
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)…
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…
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…
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…
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition
Chao Weng, Chengzhu Yu, Jia Cui +2
In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition. Specifically, initialized with a RNN-T trained model, MBR tr…
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen +43
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which…
UTD-CRSS Submission for MGB-3 Arabic Dialect Identification: Front-end and Back-end Advancements on Broadcast Speech
Ahmet E. Bulut, Qian Zhang, Chunlei Zhang +2
This study presents systems submitted by the University of Texas at Dallas, Center for Robust Speech Systems (UTD-CRSS) to the MGB-3 Arabic Dialect Identification (ADI) subtask. Th…
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation
Chunlei Zhang, Fahimeh Bahmaninezhad, Shivesh Ranjan +3
This document briefly describes the systems submitted by the Center for Robust Speech Systems (CRSS) from The University of Texas at Dallas (UTD) to the 2016 National Institute of…