Zobrazeno 1 - 10
of 294
pro vyhledávání: '"Chen Xianhong"'
Autor:
Ren Wei, Chen Xianhong
Publikováno v:
Open Archaeology, Vol 7, Iss 1, Pp 1192-1215 (2021)
This study developed a framework to evaluate, in the context of COVID-19, the performance of an OVRWCHT (online 360° virtual reality world cultural heritage tourism) system created by the authors for the purpose of heritage interpretation and presen
Externí odkaz:
https://doaj.org/article/62c5a9ff73d243b2892ed91e5e751288
Target Speaker Extraction by Directly Exploiting Contextual Information in the Time-Frequency Domain
In target speaker extraction, many studies rely on the speaker embedding which is obtained from an enrollment of the target speaker and employed as the guidance. However, solely using speaker embedding may not fully utilize the contextual information
Externí odkaz:
http://arxiv.org/abs/2402.17146
Autor:
Tang, Ke, Yao, Jin, Wang, Run, Ruan, Weiqing, Chen, Xianhong, Yang, Lingli, Li, Yueping, Zhang, Rui, Zhou, Chunlan
Publikováno v:
In Geriatric Nursing November-December 2024 60:671-676
Akademický článek
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Publikováno v:
In Behaviour Research and Therapy December 2023 171
Publikováno v:
In Speech Communication November 2023 155
Autor:
Liu, Yi, Liang, Tianyu, Xu, Can, Zhang, Xianwei, Chen, Xianhong, Zhang, Wei-Qiang, He, Liang, song, Dandan, Li, Ruyun, Wu, Yangcheng, Ouyang, Peng, Yin, Shouyi
This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge. Six subsystems
Externí odkaz:
http://arxiv.org/abs/1912.11585
In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast
Externí odkaz:
http://arxiv.org/abs/1904.11130
Akademický článek
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Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers. The parameters of PLDA are often estimated by maximizing the objective function, which fo
Externí odkaz:
http://arxiv.org/abs/1808.08344