  Topic modelling techniques such as LDA have recently been applied to speech
transcripts and OCR output. These corpora may contain noisy or erroneous texts
which may undermine topic stability. Therefore, it is important to know how
well a topic modelling algorithm will perform when applied to noisy data. In
this paper we show that different types of textual noise will have diverse
effects on the stability of different topic models. From these observations, we
propose guidelines for text corpus generation, with a focus on automatic speech
transcription. We also suggest topic model selection methods for noisy corpora.
