arXiv:2603.10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity. First, we propose a model-agnostic scoring protocol that produces continuous verification scores for both API-only and open-weight models, using confidence scores or log-likelihood ratios from the Yes/No token probabilities. Using this protocol, we benchmark recent speech-aware LLMs and o
The proliferation of speech-aware LLMs necessitates robust evaluation methods for their nuanced capabilities, especially concerning speaker identity, a previously overlooked aspect in their primary training objectives.
Evaluating LLMs' ability to encode speaker identity creates new opportunities for voice authentication, personalization, and potentially high-fidelity synthetic voice generation, with implications for security and human-AI interaction.
A new standardized protocol for evaluating speaker verification in speech-aware LLMs provides a critical benchmark, distinguishing models purely focused on linguistic content from those capable of recognizing individual speakers.
- · AI developers focused on voice biometrics
- · Customer service industries
- · Security solutions providers
- · Personalization platforms
- · Fraudsters leveraging voice synthesis without detection
- · Platforms lacking advanced speaker verification
Improved speaker verification in LLMs enhances security for voice-controlled systems and financial transactions.
This capability could lead to more personalized AI assistants that adapt to specific users based on their unique voice characteristics.
Ethical and regulatory discussions will intensify regarding deepfake voice detection and the rights associated with one's unique vocal identity.
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Read at arXiv cs.AI