Introducing voice timbre attribute detection
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.
Cite
@article{arxiv.2505.09661,
title = {Introducing voice timbre attribute detection},
author = {Jinghao He and Zhengyan Sheng and Liping Chen and Kong Aik Lee and Zhen-Hua Ling},
journal= {arXiv preprint arXiv:2505.09661},
year = {2025}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2505.09382