  As dialogue systems are becoming more and more interactional and social, also
the accurate automatic speech recognition (ASR) of conversational speech is of
increasing importance. This shifts the focus from short, spontaneous,
task-oriented dialogues to the much higher complexity of casual face-to-face
conversations. However, the collection and annotation of such conversations is
a time-consuming process and data is sparse for this specific speaking style.
This paper presents ASR experiments with read and conversational Austrian
German as target. In order to deal with having only limited resources available
for conversational German and, at the same time, with a large variation among
speakers with respect to pronunciation characteristics, we improve a
Kaldi-based ASR system by incorporating a (large) knowledge-based pronunciation
lexicon, while exploring different data-based methods to restrict the number of
pronunciation variants for each lexical entry. We achieve best WER of 0.4% on
Austrian German read speech and best average WER of 48.5% on conversational
speech. We find that by using our best pronunciation lexicon a similarly high
performance can be achieved than by increasing the size of the data used for
the language model by approx. 360% to 760%. Our findings indicate that for
low-resource scenarios -- despite the general trend in speech technology
towards using data-based methods only -- knowledge-based approaches are a
successful, efficient method.
