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Updated: Jun 24, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

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Published on: September 27, 2024

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通过双向子域对齐,通过弱源域标签进行无监督的域调整

Heng Zhou1, Ping Zhong1, Daoliang Li1

  • 1College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China; National Innovation Center for Digital Fishery, Beijing, China; Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China; Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China.

Neural networks : the official journal of the International Neural Network Society
|June 8, 2024
PubMed
概括

这项研究引入了无监督域调整 (UDA) 的新框架,该框架可以通过不可靠的源标签提高性能. 它使用代伪标签和双向子域对齐 (BSA) 来进行强大的特征提取和域对齐.

关键词:
对抗式学习是一种对抗式的学习.双向分布对齐的两个方向.班级中枢位匹配的匹配代码书 代码书 代码书域名适应领域适应弱源域名标签的弱源域名标签.

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