  Anglicisms are a challenge in German speech recognition. Due to their
irregular pronunciation compared to native German words, automatically
generated pronunciation dictionaries often include faulty phoneme sequences for
Anglicisms. In this work, we propose a multitask sequence-to-sequence approach
for grapheme-to-phoneme conversion to improve the phonetization of Anglicisms.
We extended a grapheme-to-phoneme model with a classifier to distinguish
Anglicisms from native German words. With this approach, the model learns to
generate pronunciations differently depending on the classification result. We
used our model to create supplementary Anglicism pronunciation dictionaries
that are added to an existing German speech recognition model. Tested on a
dedicated Anglicism evaluation set, we improved the recognition of Anglicisms
compared to a baseline model, reducing the word error rate by 1 % and the
Anglicism error rate by 3 %. We show that multitask learning can help solving
the challenge of Anglicisms in German speech recognition.
