  We investigate what linguistic factors affect the performance of Automatic
Speech Recognition (ASR) models. We hypothesize that orthographic and
phonological complexities both degrade accuracy. To examine this, we fine-tune
the multilingual self-supervised pretrained model Wav2Vec2-XLSR-53 on 25
languages with 15 writing systems, and we compare their ASR accuracy, number of
graphemes, unigram grapheme entropy, logographicity (how much
word/morpheme-level information is encoded in the writing system), and number
of phonemes. The results demonstrate that orthographic complexities
significantly correlate with low ASR accuracy, while phonological complexity
shows no significant correlation.
