[Submitted on 2 Aug 2026]
Title:Human-like cross-language generalisation in deep neural speaker embeddings and its acoustic foundations
View a PDF of the paper titled Human-like cross-language generalisation in deep neural speaker embeddings and its acoustic foundations, by Tianze Xu and 3 other authors
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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)
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