تقرير
Reading Miscue Detection in Primary School through Automatic Speech Recognition
| العنوان: | Reading Miscue Detection in Primary School through Automatic Speech Recognition |
|---|---|
| المؤلفون: | Gao, Lingyun, Tejedor-Garcia, Cristian, Strik, Helmer, Cucchiarini, Catia |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | Automatic reading diagnosis systems can benefit both teachers for more efficient scoring of reading exercises and students for accessing reading exercises with feedback more easily. However, there are limited studies on Automatic Speech Recognition (ASR) for child speech in languages other than English, and limited research on ASR-based reading diagnosis systems. This study investigates how efficiently state-of-the-art (SOTA) pretrained ASR models recognize Dutch native children speech and manage to detect reading miscues. We found that Hubert Large finetuned on Dutch speech achieves SOTA phoneme-level child speech recognition (PER at 23.1\%), while Whisper (Faster Whisper Large-v2) achieves SOTA word-level performance (WER at 9.8\%). Our findings suggest that Wav2Vec2 Large and Whisper are the two best ASR models for reading miscue detection. Specifically, Wav2Vec2 Large shows the highest recall at 0.83, whereas Whisper exhibits the highest precision at 0.52 and an F1 score of 0.52. Comment: Proc. INTERSPEECH 2024, 1-5 September 2024. Kos Island, Greece |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.07060 |
| رقم الأكسشن: | edsarx.2406.07060 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |