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.

在这篇研究中，我们调查了哪些语言因素会影响自动语音识别（ASR）模型的性能。我们假设正字法复杂性和音素复杂性都会降低准确性。为了验证这一假设，我们在25种语言的基础上，使用15种书写体系对多语言自我监督预训练模型Wav2Vec2-XLSR-53进行微调，并比较它们的ASR准确性、字素数量、一元字素熵、表意性（书写系统中编码了多少词/语素级信息）以及音素数量。研究结果表明，正字法复杂性与低ASR准确性显著相关，而音素复杂性则没有显著相关性。

语言复杂性与语音识别准确性：字母复杂性有害，音韵复杂性无害