no code implementations • 30 May 2023 • Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Luping Liu, Zhenhui Ye, Ziyue Jiang, Chao Weng, Zhou Zhao, Dong Yu
Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common.
no code implementations • 15 Aug 2022 • Chunlei Zhang, Dong Yu
On the basis of the pretrained CSSL model, we further propose to employ a negative sample free SSL objective (i. e., DINO) to fine-tune the speaker embedding network.
no code implementations • 6 Jun 2022 • Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli, Dong Yu
We leverage recent advancements in self-supervised speech representation learning as well as speech synthesis front-end techniques for system development.
1 code implementation • 5 Jun 2022 • Jinchuan Tian, Jianwei Yu, Chunlei Zhang, Chao Weng, Yuexian Zou, Dong Yu
Experiments conducted on Mandarin-English code-switched speech suggest that the proposed LAE is capable of discriminating different languages in frame-level and shows superior performance on both monolingual and multilingual ASR tasks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 20 May 2022 • Meng Yu, Yong Xu, Chunlei Zhang, Shi-Xiong Zhang, Dong Yu
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 the loudspeaker plays back.
1 code implementation • 11 May 2022 • Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli, Dong Yu
In our experiment on the VCTK dataset, we demonstrate that content embeddings derived from the conditional DSVAE overcome the randomness and achieve a much better phoneme classification accuracy, a stabilized vocalization and a better zero-shot VC performance compared with the competitive DSVAE baseline.
1 code implementation • 31 Mar 2022 • Soumi Maiti, Yushi Ueda, Shinji Watanabe, Chunlei Zhang, Meng Yu, Shi-Xiong Zhang, Yong Xu
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting.
1 code implementation • 30 Mar 2022 • Jiachen Lian, Chunlei Zhang, Dong Yu
A zero-shot voice conversion is performed by feeding an arbitrary speaker embedding and content embeddings to the VAE decoder.
no code implementations • 29 Nov 2021 • Brian Yan, Chunlei Zhang, Meng Yu, Shi-Xiong Zhang, Siddharth Dalmia, Dan Berrebbi, Chao Weng, Shinji Watanabe, Dong Yu
Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type.
no code implementations • 2 Apr 2021 • Meng Yu, Chunlei Zhang, Yong Xu, ShiXiong Zhang, Dong Yu
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 quality without any reference, such as in the mean opinion score (MOS) tests.
no code implementations • 16 Mar 2021 • Chunlei Zhang, Meng Yu, Chao Weng, Dong Yu
This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embedding extraction for robust speaker verification (SV).
1 code implementation • 13 Dec 2020 • Wei Xia, Chunlei Zhang, Chao Weng, Meng Yu, Dong Yu
First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examples.
1 code implementation • 3 Dec 2020 • Haohan Guo, Heng Lu, Na Hu, Chunlei Zhang, Shan Yang, Lei Xie, Dan Su, Dong Yu
In order to make timbre conversion more stable and controllable, speaker embedding is further decomposed to the weighted sum of a group of trainable vectors representing different timbre clusters.
no code implementations • 26 Nov 2020 • Jiatong Shi, Chunlei Zhang, Chao Weng, Shinji Watanabe, Meng Yu, Dong Yu
Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers.
Speech Enhancement Speech Extraction +1 Sound Audio and Speech Processing
no code implementations • 28 Nov 2019 • Chao Weng, Chengzhu Yu, Jia Cui, Chunlei Zhang, Dong Yu
In this work, we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition.
no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
no code implementations • 24 Oct 2016 • Chunlei Zhang, Fahimeh Bahmaninezhad, Shivesh Ranjan, Chengzhu Yu, Navid Shokouhi, John H. L. Hansen
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 Standards and Technology (NIST) Speaker Recognition Evaluation (SRE).