CONF Linke_LREC_2022/IDIAP Conversational Speech Recognition Needs Data? Experiments with Austrian German Linke, Julian Garner, Philip N. Kubin, Gernot Schuppler, Barbara European Language Resources Association - Proceedings of the 13th Language Resources and Evaluation Conference 2022 Marseille, France 4684--4691 http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.500.pdf URL Conversational speech represents one of the most complex of automatic speech recognition (ASR) tasks owing to the high inter-speaker variation in both pronunciation and conversational dynamics. Such complexity is particularly sensitive to low-resourced (LR) scenarios. Recent developments in self-supervision have allowed such scenarios to take advantage of large amounts of otherwise unrelated data. In this study, we characterise an (LR) Austrian German conversational task. We begin with a non-pre-trained baseline and show that fine-tuning of a model pre-trained using self-supervision leads to improvements consistent with those in the literature; this extends to cases where a lexicon and language model are included. We also show that the advantage of pre-training indeed arises from the larger database rather than the self-supervision. Further, by use of a leave-one-conversation out technique, we demonstrate that robustness problems remain with respect to inter-speaker and inter-conversation variation. This serves to guide where future research might best be focused in light of the current state-of-the-art.