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This paper compares state-of-the-art ASR systems to human listeners on recognizing diverse Dutch speech, finding that ASR systems match or exceed human performance in some cases, with Google Telephony leading. It highlights the impact of speaker age, regional accents, and test set selection on benchmarking conclusions.
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.