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 would not help since
they are pre-trained on voices that differ from children's in terms of
frequency and amplitude. Because most of these are pre-trained with data in a
specific range of amplitude, their objectives do not make them ready for voices
in different amplitudes. To overcome this issue, we added a new objective to
the 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 model
for Meaningless Words (MW) and Rapid Automatic Naming (RAN) tests. Using
masking in concatenation with RFP outperforms the masking objective of Wav2Vec
2.0 by reaching a Word Error Rate (WER) of 1.35. Our new approach reaches a 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.

介绍了一种新的针对学前儿童语音测试的方法，通过加入 Random Frequency Pitch 目标到 Wav2Vec 2.0 模型的屏蔽目标中，在 Persian CommonVoice 数据集上达到了仅有 1.35 的字错误率，并在零次和少次测试中达到了积极的成果。