arXiv cs.CL· Tianze Xu, Xiyang Li, Xiaoming Jiang, Volker Dellwo·· 11 小时前AI 评分29
深度神经说话人嵌入的类人跨语言泛化及其声学基础
Human-like cross-language generalisation in deep neural speaker embeddings and its acoustic foundations
AI 导读
研究用粤语—英语双语语音测试18个SOTA模型，发现深度神经说话人嵌入具备跨语言泛化能力，其表现与人类听感大体一致，能准确区分说话人身份并对语言切换敏感。这种泛化依赖基本共享的声学线索，但权重随语言变化，与人类模式相似，为跨语言语音相似性处理中的人机对齐提供了汇聚证据。
正文 · 原文
Abstract:Generalisability is a central question for deep neural networks, including the cross-language generalisation of speaker embeddings. This study examined whether deep neural speaker embeddings generalise across languages and how such generalisation relates to human perception and acoustic structure. Using Cantonese--English bilingual speech presented in within- and cross-language conditions, we derived similarity from 18 state-of-the-art models and compared it with human ratings and acoustic features using linear mixed models and representational similarity analysis. Embeddings exhibited cross-language generalisation that broadly paralleled human behaviour, reflecting accurate identity discrimination and sensitivity to language switching. This generalisation relied on largely shared acoustic cues with language-sensitive weighting, mirroring human patterns. Together, these findings provide converging evidence for human--machine alignment in cross-language voice similarity processing.
| Comments: | This manuscript has been accepted for presentation at Interspeech 2026. Code is available at this https URL. The archived release is available on Zenodo at this https URL |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2611.05384 [cs.CL] |
| (or arXiv:2611.05384v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05384 arXiv-issued DOI via DataCite |
Submission history
From: Tianze Xu [view email]
[v1]
Sun, 2 Aug 2026 09:26:16 UTC (119 KB)
来源：arXiv cs.CL · arxiv.org