  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.
