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20162023
most citedC3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification

24 citations · 90 across the 19 of their papers we have counts for

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13 papers · 1 filter

eess.AS2023★ 2 cited

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…

eess.AS2022★ 24 cited

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…

eess.AS2022

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…

eess.AS2022★ 8 cited

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…

eess.AS2022★ 5 cited

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…

eess.AS2022

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…

eess.AS2022★ 2 cited

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…

eess.AS2021★ 5 cited

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…

eess.AS2021

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…

eess.AS2020★ 2 cited

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…

eess.AS2020★ 13 cited

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…

eess.AS2019★ 3 cited

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…

eess.AS2017

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…