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- W4286527731 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 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." @default.
- W4286527731 created "2022-07-22" @default.
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- W4286527731 date "2022-03-24" @default.
- W4286527731 modified "2023-09-28" @default.
- W4286527731 title "Automatic Speech Recognition for Speech Assessment of Persian Preschool Children" @default.
- W4286527731 doi "https://doi.org/10.48550/arxiv.2203.12886" @default.
- W4286527731 hasPublicationYear "2022" @default.
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