Swiss German Speech-to-Text (STT) is a challenging task due to the fact that no single-dominant pronunciation or standardised orthography exists. This is compounded by a severe lack of appropriate training data. One potential avenue, and that which is investigated as part of the GermEval 2020 Task 4 on Low-Resource Speech-to-Text, is to translate spoken Swiss German into standard German text implicitly through STT. In this paper, we describe our proposed system that makes use of the Kaldi Speech Recognition Toolkit to implement a time delay neural network (TDNN) Acoustic Model (AM) with an extended pronunciation lexicon and language model. Using this approach, we achieve a word error rate of 45.45% on the held-out test set.