TY - CHAP A1 - Bayerl, SebastianP A1 - Wagner, Dominik A1 - Nöth, Elmar A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian A1 - Sojka, Petr A1 - Kopeček, Ivan A1 - Pala, Karel A1 - Horák, Aleš T1 - The Influence of Dataset Partitioning on Dysfluency Detection Systems N2 - This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems. For this, we perform experiments using wav2vec 2.0 models with a classification head as well as support vector machines (SVM) in conjunction with the features extracted from the wav2vec 2.0 model to detect dysfluencies. We train and evaluate the systems with different non-speaker-exclusive and speaker-exclusive splits of the Stuttering Events in Podcasts (SEP-28k) dataset to shed some light on the variability of results w.r.t. to the partition method used. Furthermore, we show that the SEP-28k dataset is dominated by only a few speakers, making it difficult to evaluate. To remedy this problem, we created SEP-28k-Extended (SEP-28k-E), containing semi-automatically generated speaker and gender information for the SEP-28k corpus, and suggest different data splits, each useful for evaluating other aspects of methods for dysfluency detection. KW - stuttering · dysfluencies · pathological speech · SEP-28k Y1 - 2022 U6 - https://doi.org/10.48550/arXiv.2206.03400 PB - Springer International Publishing ER -