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

2026-06-20

AI Summary

This work addresses the limitations of traditional speaker attribute prediction methods, which rely on fixed categorical labels and thus struggle to support semantic richness and zero-shot generalization. To overcome this, the authors propose an open-set prediction framework leveraging large language model (LLM) embeddings. Their approach employs a keyword-augmentation mechanism to construct compact yet discriminative semantic embedding manifolds, sharpens decision boundaries in semantically dense regions via a top-k negative example loss, and enhances semantic coherence through geometric manifold regularization. Evaluated on the LibriTTS-P dataset, the method outperforms conventional closed-set baselines and demonstrates strong zero-shot generalization capabilities on unseen synonymous attributes.

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.

Problem

• open-set speaker attribute prediction
• semantic richness
• zero-shot generalizability
• cross-modal gap
• embedding manifold

Innovation

• open-set speaker attribute prediction
• LLM embeddings
• keyword-appending strategy
• top-k negative loss
• cross-modal representation