mlsanityData to 7 Oct 2026

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

arXiv 2403.08187 ↗First posted 13 Mar 2024cs.CL cs.SD eess.AScited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

12 of 32 references.

References · 12

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589
  4. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
  5. 2015Attention-Based Models for Speech Recognitioncited by 400
  6. 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
  7. 2015End-to-End Attention-based Large Vocabulary Speech Recognitioncited by 148
  8. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  9. 2020Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languagescited by 30
  10. 2022How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communicationscited by 30
  11. 2023Adaptation of Whisper models to child speech recognitioncited by 25
  12. 2022Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessmentcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Multimodal LLMs are not all you need for Pediatric Speech Language Pathologycited by 0
  2. 2026Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learningcited by 0
  3. 2026Phoneme-Level Mispronunciation Screening in Polish-Speaking Children with an Explainable Assistantcited by 0