# UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment

arXiv: https://arxiv.org/abs/2605.23261 · HTML: https://mlsanity.com/paper/2605.23261/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2026.

## Built on by (most cited first, 2 of 2)

- [Multi-Dimensional Prosody Judgment For Live Streaming Speech Synthesis](https://mlsanity.com/paper/2609.20124/) (2026)
- [SpeechCritic: Learning a Diagnostic Speech Judge from Limited Human Preferences](https://mlsanity.com/paper/2609.34582/) (2026)

## Abstract (arXiv)

Evaluating speech generation still relies heavily on human judgments, such as Mean Opinion Score (MOS), which are expensive, subjective, and difficult to reproduce at scale. While a few recent studies have begun to explore AudioLLM-based judge models, existing efforts typically target only a narrow set of scenarios (e.g., utterance-level quality or single-turn dialogue) and provide limited coverage of diverse speech generation tasks and evaluation dimensions. In this work, we propose UniSRM, a unified speech reward model that can support multi-dimensional, interpretable reward signals with reliable reasoning. To support training and evaluation, we introduce UniSRM-Data and UniSRM-Bench, covering speech evaluation tasks from utterance-level quality to context-level coherence. Based on this dataset, we present the unified speech reward model, UniSRM, with a two-stage pipeline that enables reasoning-based fine-grained assessment. Furthermore, we introduce Reasoning-Consistent Rewards to improve the reliability of the reasoning process. Experiments show that UniSRM delivers more reliable and human-aligned judgments across a broad range of speech evaluation tasks, offering a practical foundation for scalable and unified evaluation of speech quality.