# Automatic Speech Recognition (ASR) for the Diagnosis of pronunciation of Speech Sound Disorders in Korean children

arXiv: https://arxiv.org/abs/2403.08187 · HTML: https://mlsanity.com/paper/2403.08187/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [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)
- [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)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://mlsanity.com/paper/2109.11680/) (2021)
- [Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages](https://mlsanity.com/paper/2012.12121/) (2020)
- [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://mlsanity.com/paper/2203.16822/) (2022)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessment](https://mlsanity.com/paper/2203.15937/) (2022)

## Built on by (most cited first, 3 of 3)

- [Multimodal LLMs are not all you need for Pediatric Speech Language Pathology](https://mlsanity.com/paper/2604.26568/) (2026)
- [Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning](https://mlsanity.com/paper/2606.10213/) (2026)
- [Phoneme-Level Mispronunciation Screening in Polish-Speaking Children with an Explainable Assistant](https://mlsanity.com/paper/2606.25181/) (2026)

## Abstract (arXiv)

This study presents a model of automatic speech recognition (ASR) designed to diagnose pronunciation issues in children with speech sound disorders (SSDs) to replace manual transcriptions in clinical procedures. Since ASR models trained for general purposes primarily predict input speech into real words, employing a well-known high-performance ASR model for evaluating pronunciation in children with SSDs is impractical. We fine-tuned the wav2vec 2.0 XLS-R model to recognize speech as pronounced rather than as existing words. The model was fine-tuned with a speech dataset from 137 children with inadequate speech production pronouncing 73 Korean words selected for actual clinical diagnosis. The model's predictions of the pronunciations of the words matched the human annotations with about 90% accuracy. While the model still requires improvement in recognizing unclear pronunciation, this study demonstrates that ASR models can streamline complex pronunciation error diagnostic procedures in clinical fields.