SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgement

• URL: http://arxiv.org/abs/2508.20916v2
• Date: Mon, 10 Nov 2025 07:45:48 GMT
• Title: SageLM: A Multi-aspect and Explainable Large Language Model for Speech Judgement
• Authors: Yuan Ge, Junxiang Zhang, Xiaoqian Liu, Bei Li, Xiangnan Ma, Chenglong Wang, Kaiyang Ye, Yangfan Du, Linfeng Zhang, Yuxin Huang, Tong Xiao, Zhengtao Yu, JingBo Zhu,
• Abstract summary: Speech-to-Speech (S2S) Large Language Models (LLMs) are foundational to natural human-computer interaction.<n>We propose textttSageLM, an end-to-end, multi-aspect, and explainable speech LLM for comprehensive S2S LLMs evaluation.
• Score: 74.51476422119457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech-to-Speech (S2S) Large Language Models (LLMs) are foundational to natural human-computer interaction, enabling end-to-end spoken dialogue systems. However, evaluating these models remains a fundamental challenge. We propose \texttt{SageLM}, an end-to-end, multi-aspect, and explainable speech LLM for comprehensive S2S LLMs evaluation. First, unlike cascaded approaches that disregard acoustic features, SageLM jointly assesses both semantic and acoustic dimensions. Second, it leverages rationale-based supervision to enhance explainability and guide model learning, achieving superior alignment with evaluation outcomes compared to rule-based reinforcement learning methods. Third, we introduce \textit{SpeechFeedback}, a synthetic preference dataset, and employ a two-stage training paradigm to mitigate the scarcity of speech preference data. Trained on both semantic and acoustic dimensions, SageLM achieves an 82.79\% agreement rate with human evaluators, outperforming cascaded and SLM-based baselines by at least 7.42\% and 26.20\%, respectively.

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