  Dysarthria, a common issue among stroke patients, severely impacts speech
intelligibility. Inappropriate pauses are crucial indicators in severity
assessment and speech-language therapy. We propose to extend a large-scale
speech recognition model for inappropriate pause detection in dysarthric
speech. To this end, we propose task design, labeling strategy, and a speech
recognition model with an inappropriate pause prediction layer. First, we treat
pause detection as speech recognition, using an automatic speech recognition
(ASR) model to convert speech into text with pause tags. According to the newly
designed task, we label pause locations at the text level and their
appropriateness. We collaborate with speech-language pathologists to establish
labeling criteria, ensuring high-quality annotated data. Finally, we extend the
ASR model with an inappropriate pause prediction layer for end-to-end
inappropriate pause detection. Moreover, we propose a task-tailored metric for
evaluating inappropriate pause detection independent of ASR performance. Our
experiments show that the proposed method better detects inappropriate pauses
in dysarthric speech than baselines. (Inappropriate Pause Error Rate: 14.47%)
