Automatic Speech Recognition for Speech Assessment of Persian Preschool Children

Abstract

Preschool evaluation is crucial because it gives teachers and parents influential knowledge about children's growth and development. The COVID-19 pandemic has highlighted the necessity of online assessment for preschool children. One of the areas that should be tested is their ability to speak. Employing an Automatic Speech Recognition(ASR) system is useless since they are pre-trained on voices that are different from children's voices in terms of frequency and amplitude. We constructed an ASR for our cognitive test system to solve this issue using the Wav2Vec 2.0 model with a new pre-training objective called Random Frequency Pitch(RFP). In addition, we used our new dataset to fine-tune our model for Meaningless Words(MW) and Rapid Automatic Naming(RAN) tests. Our new approach reaches a Word Error Rate(WER) of 6.45 on the Persian section of the CommonVoice dataset. Furthermore, our novel methodology produces positive outcomes in zero- and few-shot scenarios.Comment: 8 pages, 5 figures, 4 tables, 1 algorith