We describe our submission to the IWSLT 2026 Speech Translation Metrics shared task, which targets reference-free quality estimation for English-to-German and English-to-Chinese speech translation. Our primary system combines COMETKiwi-22, applied to ASR transcripts, with a lightweight post-processing step called tie calibration: a learned score-bucketing that collapses near-identical scores into exact ties, reducing noisy within-document pairwise ranking errors. On the official development set the method achieves a segment-level Kendall tau-b of 39.4% on average, compared to 34.6% for plain COMETKiwi, 29.2% for SpeechQE, and 24.4% for BLASER 2.0 QE. System-level Soft Pairwise Accuracy is 88.0%, comparable to COMETKiwi (89.4%) and above SpeechQE (86.0%). The method requires no audio, no retraining, and one hyperparameter per target language tuned entirely on the training split.