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arXiv · 2606.21979

Toward Open-Set Speaker Attribute Prediction with Keyword-Appended LLM Embeddings

Byoungjun So·Jaejun Lee·Kyogu Lee
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Abstract

Understanding speaker attributes is crucial for voice-related applications, yet conventional approaches rely on fixed categorical labels, lacking semantic richness and zero-shot generalizability. We propose a novel framework for open-set speaker attribute prediction leveraging Large Language Model (LLM) embeddings to represent attributes in a continuous semantic space. To bridge the cross-modal gap, we introduce a keyword-appending strategy that structures broad semantic representations into a compact, discriminative manifold. Furthermore, we employ a top-k negative loss to establish robust decision boundaries in crowded semantic regions. Experimental results on LibriTTS-P demonstrate that our method outperforms closed-set benchmarks and generalizes effectively to unseen synonyms. Geometric analysis suggests that our strategies regularize the embedding manifold, balancing semantic cohesion with predictive clarity.

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Byoungjun So, Jaejun Lee, Kyogu Lee. 2026-06-20. Toward Open-Set Speaker Attribute Prediction with Keyword-Appended LLM Embeddings. https://arxiv.org/abs/2606.21979

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