Introducing voice timbre attribute detection

• URL: http://arxiv.org/abs/2505.09661v2
• Date: Sun, 22 Jun 2025 11:25:43 GMT
• Title: Introducing voice timbre attribute detection
• Authors: Jinghao He, Zhengyan Sheng, Liping Chen, Kong Aik Lee, Zhen-Hua Ling,
• Abstract summary: This paper focuses on explaining the timbre conveyed by speech signals and introduces a task termed voice timbre attribute detection (vTAD)<n>A pair of speech utterances is processed, and their intensity is compared in a designated timbre descriptor.<n>A framework is proposed, which is built upon the speaker embeddings extracted from the speech utterances.
• Score: 40.14712328633083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper focuses on explaining the timbre conveyed by speech signals and introduces a task termed voice timbre attribute detection (vTAD). In this task, voice timbre is explained with a set of sensory attributes describing its human perception. A pair of speech utterances is processed, and their intensity is compared in a designated timbre descriptor. Moreover, a framework is proposed, which is built upon the speaker embeddings extracted from the speech utterances. The investigation is conducted on the VCTK-RVA dataset. Experimental examinations on the ECAPA-TDNN and FACodec speaker encoders demonstrated that: 1) the ECAPA-TDNN speaker encoder was more capable in the seen scenario, where the testing speakers were included in the training set; 2) the FACodec speaker encoder was superior in the unseen scenario, where the testing speakers were not part of the training, indicating enhanced generalization capability. The VCTK-RVA dataset and open-source code are available on the website https://github.com/vTAD2025-Challenge/vTAD.

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