The Influence of Dataset Partitioning on Dysfluency Detection Systems
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.