Proposes keyword-appended LLM embeddings plus top-k negative loss for open-set speaker attribute prediction that outperforms closed-set baselines on LibriTTS-P and generalizes to unseen synonyms.
Toward Open-Set Speaker Attribute Prediction with Keyword-Appended LLM Embeddings
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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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Toward Open-Set Speaker Attribute Prediction with Keyword-Appended LLM Embeddings
Proposes keyword-appended LLM embeddings plus top-k negative loss for open-set speaker attribute prediction that outperforms closed-set baselines on LibriTTS-P and generalizes to unseen synonyms.