  Stuttering is a varied speech disorder that harms an individual's
communication ability. Persons who stutter (PWS) often use speech therapy to
cope with their condition. Improving speech recognition systems for people with
such non-typical speech or tracking the effectiveness of speech therapy would
require systems that can detect dysfluencies while at the same time being able
to detect speech techniques acquired in therapy. This paper shows that
fine-tuning wav2vec 2.0 [1] for the classification of stuttering on a sizeable
English corpus containing stuttered speech, in conjunction with multi-task
learning, boosts the effectiveness of the general-purpose wav2vec 2.0 features
for detecting stuttering in speech; both within and across languages. We
evaluate our method on FluencyBank , [2] and the German therapy-centric Kassel
State of Fluency (KSoF) [3] dataset by training Support Vector Machine
classifiers using features extracted from the finetuned models for six
different stuttering-related event types: blocks, prolongations, sound
repetitions, word repetitions, interjections, and - specific to therapy -
speech modifications. Using embeddings from the fine-tuned models leads to
relative classification performance gains up to 27% w.r.t. F1-score.
