ASTAR-NTU solution to AudioMOS Challenge 2025 Track1
2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , 1–4 , 2025
Abstract
Evaluation of text-to-music systems is constrained by the cost and availability of collecting experts for assessment. AudioMOS 2025 Challenge track 1 is created to automatically predict music impression (MI) as well as text alignment (TA) between the prompt and the generated musical piece. This paper reports our winning system, which uses a dual-branch architecture with pre-trained MuQ and RoBERTa models as audio and text encoders. A cross-attention mechanism fuses the audio and text representations. For training, we reframe the MI and TA prediction as a classification task. To incorporate the ordinal nature of MOS scores, one-hot labels are converted to a soft distribution using a Gaussian kernel. On the official test set, a single model trained with this method achieves a system-level Spearman’s Rank Correlation Coefficient (SRCC) of 0.991 for MI and 0.952 for TA, corresponding to a relative improvement of 21.21% in MI SRCC and 31.47% in TA SRCC over the challenge baseline.
BibTeX
@inproceedings{rittergutierrez2025astarntu,
title = {ASTAR-NTU solution to AudioMOS Challenge 2025 Track1},
author = {Ritter-Gutierrez, Fabian and Lin, Yi-Cheng and Wei, Jui-Chiang and Wong, Jeremy H. M. and Chen, Nancy F. and Lee, Hung-yi},
booktitle = {2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
year = {2025},
pages = {1--4},
doi = {10.1109/ASRU65441.2025.11434795},
}