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This paper presents the system submitted to Track 1 of the Voice Timbre Attribute Detection (vTAD) 2025 Challenge. The core objective of the vTAD challenge is to address the intensity comparison task, which requires determining the relative strength of timbre attributes between two speech signals in dimensions of human perception. The system utilizes pre-trained speaker representations and gender representations as front-end inputs, and employs a residual neural network to output the intensity comparison results of speech pairs under specific descriptors. The system ultimately secured third place on the Seen track of the vTAD 2025 Challenge, achieving an accuracy of 95. 38% and an equal error rate (EER) of 4. 98%.

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A Timbre Attribute Discrimination System Fusing Pre-trained Speaker Feature Extractors with Gender Prior Features

  • 会议论文

  • 发表日期：

    2026年

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  • Mengyuan Deng,
  • Jing Wang,
  • Yue Lang,
  • Shenghui Zhao,
  • Xiang Xie

摘要

This paper presents the system submitted to Track 1 of the Voice Timbre Attribute Detection (vTAD) 2025 Challenge. The core objective of the vTAD challenge is to address the intensity comparison task, which requires determining the relative strength of timbre attributes between two speech signals in dimensions of human perception. The system utilizes pre-trained speaker representations and gender representations as front-end inputs, and employs a residual neural network to output the intensity comparison results of speech pairs under specific descriptors. The system ultimately secured third place on the Seen track of the vTAD 2025 Challenge, achieving an accuracy of 95. 38% and an equal error rate (EER) of 4. 98%.