This research addresses limitations in speech naturalness evaluation by introducing a reasoning-based reward model. The system decomposes evaluations into an interpretable acoustic feature extraction stage followed by feature-grounded chain-of-thought reasoning. The authors developed their approach using 31,000 expert ratings and tested it against real-world speech interactions. Results indicate the model achieves performance metrics approaching human consistency levels and improves speech generation quality when applied to reinforcement learning from human feedback processes.