  High quality Automatic Speech Recognition (ASR) is a prerequisite for
speech-based applications and research. While state-of-the-art ASR software is
freely available, the language dependent acoustic models are lacking for
languages other than English, due to the limited amount of freely available
training data. We train acoustic models for German with Kaldi on two datasets,
which are both distributed under a Creative Commons license. The resulting
model is freely redistributable, lowering the cost of entry for German ASR. The
models are trained on a total of 412 hours of German read speech data and we
achieve a relative word error reduction of 26% by adding data from the Spoken
Wikipedia Corpus to the previously best freely available German acoustic model
recipe and dataset. Our best model achieves a word error rate of 14.38 on the
Tuda-De test set. Due to the large amount of speakers and the diversity of
topics included in the training data, our model is robust against speaker
variation and topic shift.
