  Our study presents a dialect normalization method for different Finland
Swedish dialects covering six regions. We tested 5 different models, and the
best model improved the word error rate from 76.45 to 28.58. Contrary to
results reported in earlier research on Finnish dialects, we found that
training the model with one word at a time gave best results. We believe this
is due to the size of the training data available for the model. Our models are
accessible as a Python package. The study provides important information about
the adaptability of these methods in different contexts, and gives important
baselines for further study.
