TS-SP: Learning Speaker-Preserving Representations in Audio Large Language Models

Junjie Li; Zheng Liang; Zhe Li; Tianchi Liu; Kong Aik Lee

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arXiv · 2610.04887

TS-SP: Learning Speaker-Preserving Representations in Audio Large Language Models

Junjie Li·Zheng Liang·Zhe Li·Tianchi Liu·Kong Aik Lee

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Abstract

Audio large language models (ALLMs) can understand speech content, yet their ability to use speaker identity for verification remains limited. We propose TS-SP (Two-Stage Speaker Preservation), a parameter-efficient framework for learning speaker-preserving representations and making them accessible to an ALLM's language-model component. We instantiate and evaluate TS-SP on Qwen2.5-Omni-7B. First, we adapt the audio encoder with speaker identity supervision. We then freeze the adapted encoder and train the language model to compare speakers. Both stages use low-rank adaptation (LoRA), keeping the pretrained base weights fixed. On Vox1-O, TS-SP reduces the equal error rate (EER) from 7.01\% for the Paired Loss Adaptation Baseline to 4.37\%. EER remains within 4.31--4.79\% under unseen prompts. Cross-domain evaluation on CN-Celeb yields a similar EER to the baseline, but lower accuracy at the native decision threshold. These findings support two-stage adaptation for improving speaker verification on the evaluated backbone.

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BibTeX RIS

Junjie Li, Zheng Liang, Zhe Li, Tianchi Liu, Kong Aik Lee. 2026-10-04. TS-SP: Learning Speaker-Preserving Representations in Audio Large Language Models. https://arxiv.org/abs/2610.04887

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