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.

本文研究了不同的数据分割和分割策略对口吃检测系统性能的影响，并使用 wav2vec 2.0 模型和支持矢量机 (SVM) 来检测口吃现象。我们使用 Stuttering Events in Podcasts (SEP-28k) 数据集中的不同非专属和专属分割进行训练和评估，以揭示结果与使用的分区方法的变异性，并证明 SEP-28k 数据集只由很少的演讲者占据，难以评估。为了解决这个问题，我们创建了 SEP-28k-Extended (SEP-28k-E)，其中包含 SEP-28k 语料库的半自动生成的演讲者和性别信息，并建议不同的数据分割，每个分割都有助于评估口吃检测方法的其他方面。