  We present an in-depth evaluation of four commercially available
Speech-to-Text (STT) systems for Swiss German. The systems are anonymized and
referred to as system a-d in this report. We compare the four systems to our
STT model, referred to as FHNW from hereon after, and provide details on how we
trained our model. To evaluate the models, we use two STT datasets from
different domains. The Swiss Parliament Corpus (SPC) test set and a private
dataset in the news domain with an even distribution across seven dialect
regions. We provide a detailed error analysis to detect the three systems'
strengths and weaknesses. This analysis is limited by the characteristics of
the two test sets. Our model scored the highest bilingual evaluation understudy
(BLEU) on both datasets. On the SPC test set, we obtain a BLEU score of 0.607,
whereas the best commercial system reaches a BLEU score of 0.509. On our
private test set, we obtain a BLEU score of 0.722 and the best commercial
system a BLEU score of 0.568.
