  When applying automated speech recognition (ASR) for Belgian Dutch (Van Dyck
et al. 2021), the output consists of an unsegmented stream of words, without
any 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 we
know there is no publicly available punctuation insertion system for Dutch that
functions at a usable level. The model we present here is an extension of the
models of Guhr et al. (2021) for Dutch and is made publicly available. We
trained a sequence classification model, based on the Dutch language model
RobBERT (Delobelle et al. 2020). For every word in the input sequence, the
models predicts a punctuation marker that follows the word. We have also
extended a multilingual model, for cases where the language is unknown or where
code switching applies. When performing the task of segmentation, the
application of the best models onto out of domain test data, a sliding window
of 200 words of the ASR output stream is sent to the classifier, and
segmentation is applied when the system predicts a segmenting punctuation sign
with a ratio above threshold. Results show to be much better than a machine
translation baseline approach.
