# You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish

arXiv: https://arxiv.org/abs/2405.13379 · HTML: https://mlsanity.com/paper/2405.13379/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [English Conversational Telephone Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1703.02136/) (2017)
- [A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech](https://mlsanity.com/paper/1904.12403/) (2019)

## Built on by (most cited first, 5 of 5)

- [Measuring the Accuracy of Automatic Speech Recognition Solutions](https://mlsanity.com/paper/2408.16287/) (2024)
- [Is Semi-Automatic Transcription Useful in Corpus Creation? Preliminary Considerations on the KIParla Corpus](https://mlsanity.com/paper/2603.16258/) (2026)
- [A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English](https://mlsanity.com/paper/2603.24549/) (2026)
- [Convex Low-resource Accent-Robust Language Detection in Speech Recognition](https://mlsanity.com/paper/2605.23235/) (2026)
- [Linguistic Distance Segregates Latent Representations in Automatic Speech Recognition Systems](https://mlsanity.com/paper/2608.30853/) (2026)

## Abstract (arXiv)

The performance of Automatic Speech Recognition (ASR) systems has constantly increased in state-of-the-art development. However, performance tends to decrease considerably in more challenging conditions (e.g., background noise, multiple speaker social conversations) and with more atypical speakers (e.g., children, non-native speakers or people with speech disorders), which signifies that general improvements do not necessarily transfer to applications that rely on ASR, e.g., educational software for younger students or language learners. In this study, we focus on the gap in performance between recognition results for native and non-native, read and spontaneous, Swedish utterances transcribed by different ASR services. We compare the recognition results using Word Error Rate and analyze the linguistic factors that may generate the observed transcription errors.