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Preschool evaluation is crucial because it gives teachers and parentsinfluential knowledge about children's growth and development. The COVID-19pandemic has highlighted the necessity of online assessment for preschoolchildren. One of the areas that should be tested is their ability to speak.Employing an Automatic Speech Recognition (ASR) system would not help sincethey are pre-trained on voices that differ from children's in terms offrequency and amplitude. Because most of these are pre-trained with data in aspecific range of amplitude, their objectives do not make them ready for voicesin different amplitudes. To overcome this issue, we added a new objective tothe masking objective of the Wav2Vec 2.0 model called Random Frequency Pitch(RFP). In addition, we used our newly introduced dataset to fine-tune our modelfor Meaningless Words (MW) and Rapid Automatic Naming (RAN) tests. Usingmasking in concatenation with RFP outperforms the masking objective of Wav2Vec2.0 by reaching a Word Error Rate (WER) of 1.35. Our new approach reaches a WERof 6.45 on the Persian section of the CommonVoice dataset. Furthermore, ournovel methodology produces positive outcomes in zero- and few-shot scenarios.

Automatic Speech Recognition for Speech Assessment of Persian Preschool Children