A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a
  Multi-label Problem
Sebastian P. Bayerl Dominik Wagner Ilja Baumann Florian Hönig Tobias Bocklet Elmar Nöth Korbinian Riedhammer
Most stuttering detection and classification research has viewed stuttering
as a multi-class classification problem or a binary detection task for each
dysfluency type; however, this does not match the nature of stuttering, in
which one dysfluency seldom comes alone but rather co-occurs with others. This
paper explores multi-language and cross-corpus end-to-end stuttering detection
as a multi-label problem using a modified wav2vec 2.0 system with an
attention-based classification head and multi-task learning. We evaluate the
method using combinations of three datasets containing English and German
stuttered speech, one containing speech modified by fluency shaping. The
experimental results and an error analysis show that multi-label stuttering
detection systems trained on cross-corpus and multi-language data achieve
competitive results but performance on samples with multiple labels stays below
over-all detection results.