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When applying automated speech recognition (ASR) for Belgian Dutch (Van Dycket al. 2021), the output consists of an unsegmented stream of words, withoutany punctuation. A next step is to perform segmentation and insert punctuation,making the ASR output more readable and easy to manually correct. As far as weknow there is no publicly available punctuation insertion system for Dutch thatfunctions at a usable level. The model we present here is an extension of themodels of Guhr et al. (2021) for Dutch and is made publicly available. Wetrained a sequence classification model, based on the Dutch language modelRobBERT (Delobelle et al. 2020). For every word in the input sequence, themodels predicts a punctuation marker that follows the word. We have alsoextended a multilingual model, for cases where the language is unknown or wherecode switching applies. When performing the task of segmentation, theapplication of the best models onto out of domain test data, a sliding windowof 200 words of the ASR output stream is sent to the classifier, andsegmentation is applied when the system predicts a segmenting punctuation signwith a ratio above threshold. Results show to be much better than a machinetranslation baseline approach.

FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers