  We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss
German speech, annotated with Standard German text at the sentence level. The
data is collected using a web app in which the speakers are shown Standard
German sentences, which they translate to Swiss German and record. We make the
corpus publicly available. It contains 343 hours of speech from all dialect
regions and is the largest public speech corpus for Swiss German to date.
Application areas include automatic speech recognition (ASR), text-to-speech,
dialect identification, and speaker recognition. Dialect information, age
group, and gender of the 316 speakers are provided. Genders are equally
represented and the corpus includes speakers of all ages. Roughly the same
amount of speech is provided per dialect region, which makes the corpus ideally
suited for experiments with speech technology for different dialects. We
provide training, validation, and test splits of the data. The test set
consists of the same spoken sentences for each dialect region and allows a fair
evaluation of the quality of speech technologies in different dialects. We
train an ASR model on the training set and achieve an average BLEU score of
74.7 on the test set. The model beats the best published BLEU scores on 2 other
Swiss German ASR test sets, demonstrating the quality of the corpus.
