# Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn't

arXiv: https://arxiv.org/abs/2406.09202 · HTML: https://mlsanity.com/paper/2406.09202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [PyThaiNLP: Thai Natural Language Processing in Python](https://mlsanity.com/paper/2312.04649/) (2023)
- [Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages](https://mlsanity.com/paper/2305.12606/) (2023)
- [Rank-frequency relation for Chinese characters](https://mlsanity.com/paper/1309.1536/) (2013)

## Built on by (most cited first, 4 of 4)

- [LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration](https://mlsanity.com/paper/2412.15299/) (2024)
- [Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties](https://mlsanity.com/paper/2601.04373/) (2026)
- [Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan](https://mlsanity.com/paper/2603.26248/) (2026)
- [Pretrained self-supervised speech models can recognize unseen consonants](https://mlsanity.com/paper/2606.11542/) (2026)

## Abstract (arXiv)

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.