Knowledge-light Letter-to-Sound Conversion for Swedish with FST and TBL

This paper describes some exploratory attempts to apply a combination of finite state transducers (FST) and transformation-based learning (TBL, Brill 1992) to the problem of letter-to-sound (LTS) conversion for Swedish. Following Bouma (2000) for Dutch, we employ FST for segmentation of the textual input into groups of letters and a first transcription stage; we feed the output of this step into a TBL system. With this setup, we reach 96.2% correctly transcribed segments with rather restricted means (a small set of hand-crafted rules for the FST stage; a set of 12 templates and a training set of 30kw for the TBL stage). Observing that quantity is the major error source and that compound morpheme boundaries can be useful for inferring quantity, we exploratively add good precision-low recall compound splitting based on graphotactic constraints. With this simple-minded method, targeting only a subset of the compounds, performance improves to 96.9%