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arXiv · 2505.09661

Introducing voice timbre attribute detection

Jinghao He·Zhengyan Sheng·Liping Chen·Kong Aik Lee·Zhen-Hua Ling
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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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Jinghao He, Zhengyan Sheng, Liping Chen, Kong Aik Lee, Zhen-Hua Ling. 2025-05-14. Introducing voice timbre attribute detection. https://arxiv.org/abs/2505.09661

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