KOMOS: Automatic Mean Opinion Score Estimation for Korean Speech

As speech generation and real-time spoken dialogue systems advance, neural mean opinion score (MOS) predictors are increasingly used to evaluate speech quality. However, many existing predictors are trained primarily on English read speech and generalize inconsistently across languages and speaking styles. In particular, their reliability on Korean speech has not been adequately validated. We propose KOMOS, a Korean MOS predictor for evaluating the overall quality of both read and spontaneous speech. To train and evaluate KOMOS, we collect 5,000 ratings from 29 native Korean listeners for 1,000 utterances spanning real recordings, synthesized speech, and degraded speech. KOMOS achieves an utterance-level Spearman rank correlation (SRCC) of 0.900, compared with 0.703 for UTMOS v1 and 0.767 for UTMOS v2. The inference code is publicly available at https://github.com/upskyy/komos and the trained model at https://huggingface.co/upskyy/komos.