  In this case study we trained and published a state-of-the-art open-source
model for Automatic Speech Recognition (ASR) for German to evaluate the current
potential of this technology for the use in the larger context of Digital
Humanities and cultural heritage indexation. Along with this paper we publish
our wav2vec2 based speech to text model while we evaluate its performance on a
corpus of historical recordings we assembled compared against commercial
cloud-based and proprietary services. While our model achieves moderate
results, we see that proprietary cloud services fare significantly better. As
our results show, recognition rates over 90 percent can currently be achieved,
however, these numbers drop quickly once the recordings feature limited audio
quality or use of non-every day or outworn language. A big issue is the high
variety of different dialects and accents in the German language. Nevertheless,
this paper highlights that the currently available quality of recognition is
high enough to address various use cases in the Digital Humanities. We argue
that ASR will become a key technology for the documentation and analysis of
audio-visual sources and identify an array of important questions that the DH
community and cultural heritage stakeholders will have to address in the near
future.
