Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages
arXiv:2607.01161v1 Announce Type: cross Abstract: Cross-lingual speaker verification (SV) systems typically exhibit performance degradation when enrollment and test utterances are spoken in different languages. However, standard evaluation protocols confound language mismatch with inter-speaker variability, as evaluation is generally performed with different speakers across languages. In this work, we introduce a bilingual same-speaker evaluation set for five Iberian languages, enabling analysis of cross-lingual SV under constant speaker identity. We apply this setup to a HuBERT-based SV syste
The continuous research and development in AI for speech processing lead to specialized challenges like cross-lingual speaker verification gaining focused attention.
Improving cross-lingual speaker verification is crucial for robust voice AI systems, enhancing security and user experience across diverse linguistic environments.
This research introduces a novel evaluation method that could lead to more accurate and reliable multilingual voice biometric systems by disentangling language and speaker effects.
- · AI developers
- · Multilingual organizations
- · Security companies
- · Iberian language speakers
- · Current less accurate cross-lingual speaker verification systems
More accurate cross-lingual speaker verification models will emerge, leading to better product performance.
Enhanced cross-lingual voice authentication could expand the market for voice-controlled devices and services globally.
Improved voice biometrics might raise new discussions about privacy and identity in a deeply interconnected, multilingual digital world.
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