L-Proto: Language-Aware Episodic Prototypical Training for Multilingual Speaker Verification
arXiv:2606.17416v1 Announce Type: cross Abstract: Multilingual speaker verification remains challenging because language-dependent acoustic variability causes speaker identity to become entangled with linguistic characteristics, degrading generalization across languages. In multilingual training, embeddings often encode language cues with speaker identity, causing speakers to form language-specific clusters. We propose L-Proto, a language-aware episodic prototypical training strategy that constructs language-consistent episodes. By sampling speakers from a single language per episode, L-Proto
The proliferation of AI models interacting with diverse global populations necessitates more robust multilingual capabilities, driving research into language-aware AI architectures.
Improving multilingual speaker verification is crucial for ubiquitous, secure, and globally accessible AI systems, particularly in areas like voice assistants, security, and customer service.
Speaker verification systems could become significantly more accurate and reliable across different languages, reducing bias and improving generalization for AI applications in diverse linguistic environments.
- · AI developers focused on global markets
- · Multinational corporations
- · Security and authentication platforms
- · Customers of multilingual AI services
- · AI systems with language-biased verification
Enhanced security and user experience for multilingual voice-controlled interfaces and verification systems.
Increased adoption of AI services in non-English speaking markets due to improved reliability and reduced linguistic barriers.
Potential for new business models and services built on highly accurate, language-agnostic speaker recognition.
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Read at arXiv cs.AI