UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment
Abstract
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.
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Research summary
The main contribution of this paper is the introduction of UniSRM, a unified speech reward model that provides multi-dimensional, interpretable assessments for speech evaluation, supported by a comprehensive dataset and benchmark. This work significantly advances the state of the art in speech quality evaluation, addressing critical limitations of existing models and offering a practical foundation for future research and applications in the field.
Methodology
The methodology presented in this paper is robust and innovative, introducing UniSRM, a unified speech reward model that leverages a two-stage training approach combining supervised fine-tuning (SFT) and reinforcement learning (RL) with reasoning-consistent rewards (RCR-GRPO). The model's architecture allows for multi-dimensional evaluations of speech quality, addressing key limitations in existing models. The introduction of UniSRM-Data and UniSRM-Bench provides a comprehensive dataset and benchmark for evaluating speech generation, which is a significant contribution to the field. The explicit decomposition of speech quality assessment into multiple dimensions enhances interpretability and aligns model outputs with human judgments.
Results & evaluation
The experimental evaluation is thorough, demonstrating the effectiveness of UniSRM across various tasks, including utterance-level preference judgments, fine-grained quality assessments, scenario-aware evaluations, and multi-turn dialogue assessments. The results show that UniSRM outperforms existing models in terms of accuracy and human alignment, indicating its practical applicability. The use of diverse datasets and rigorous evaluation metrics (e.g., accuracy and Pearson correlation coefficient) strengthens the findings. The ablation studies further validate the necessity of RCR-GRPO in improving model performance.
Reproducibility
The paper provides detailed implementation details, including model configurations, training hyperparameters, and dataset statistics, which enhance reproducibility. The authors also commit to releasing the datasets and code, which is crucial for enabling other researchers to replicate their work. However, the computational cost associated with training and inference may pose challenges for broader accessibility.
Limitations
The paper acknowledges limitations, such as the restricted coverage of challenging speech scenarios (e.g., heavy accents and overlapping speech) and the computational demands of the proposed model. These factors may limit the scalability and practical deployment of UniSRM in real-world applications. Additionally, while the benchmarks are comprehensive, they may not encompass all potential evaluation scenarios in speech generation.
Broader impact
The development of a unified and interpretable speech reward model has significant implications for the field of speech generation and evaluation. By providing a scalable and reliable framework for assessing speech quality, UniSRM can facilitate advancements in various applications, including virtual assistants, automated customer service, and content creation. The model's ability to produce human-aligned judgments can enhance user experience and trust in AI-generated speech systems. The main contribution of this paper is the introduction of UniSRM, a unified speech reward model that provides multi-dimensional, interpretable assessments for speech evaluation, supported by a comprehensive dataset and benchmark. This work significantly advances the state of the art in speech quality evaluation, addressing critical limitations of existing models and offering a practical foundation for future research and applications in the field.
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