Inappropriate Pause

Inappropriate pauses in speech, encompassing both silent pauses and filled pauses (e.g., "um," "uh"), are a significant area of research focusing on improving their detection and generation in various applications. Current efforts leverage advanced machine learning models, including transformer-based architectures and pre-trained language models, to analyze pause duration, location, and appropriateness within the context of speech. This research is crucial for enhancing speech recognition systems, particularly for individuals with dysarthria, improving the naturalness of text-to-speech synthesis, and developing more accurate diagnostic tools for neurological conditions like ALS. Ultimately, improved understanding and modeling of inappropriate pauses will lead to more effective communication technologies and clinical applications.