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This paper compares existing Dutch syllabification algorithms and introduces a deep learning model that combines phonetic and orthographic information, achieving a slight improvement in word accuracy.
This paper evaluates nine ASR models (Whisper, Parakeet, Wav2Vec2) on Dutch child speech datasets JASMIN and DART, finding that fine-tuned Whisper-medium achieves the best performance (WER 5.54% on JASMIN, 70.37% on DART). It also proposes a selection method to automatically identify correctly pronounced utterances with high precision, reducing the need for manual verification.