###### Abstract  
Speech is critical for interpersonal communication, but not everyone has fluent communication skills. Speech disfluency, including stuttering and interruptions, affects not only emotional expression but also clarity of expression for people who stutter. Existing methods for detecting speech disfluency rely heavily on annotated data, which can be costly. Additionally, these methods have not considered the issue of variable-length disfluent speech, which limits the scalability of detection methods. To address these limitations, this paper proposes an automated method for detecting speech disfluency that can improve communication skills for individuals and assist therapists in tracking the progress of stuttering patients. The proposed method focuses on detecting four types of disfluency features using single-task detection and utilizes embeddings from the pre-trained wav2 vec2.0 model, as well as convolutional neural network (CNN) and Transformer models for feature extraction. The model's scalability is improved by considering the issue of variable-length disfluent speech and modifying the model based on the entropy invariance of attention mechanisms. The proposed automated method for detecting speech disfluency has the potential to assist individuals in overcoming speech disfluency, improve their communication skills, and aid therapists in tracking the progress of stuttering patients. Additionally, the model's scalability across languages and lengths enhances its practical applicability. The experiments demonstrate that the model outperforms baseline models in both English and Chinese datasets, proving its universality and scalability in real-world applications. speech disfluency detection; stuttering; limited data; wav2 vec2.0; entropy invariance +
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stuttering population. Normal disfluency refers to the situation of speech disfluency that exists in people's daily lives, such as interruptions, pauses, or repetitions, which are related to people's emotional excitement, anxiety, or nervousness. Its main characteristics include phrase repetition, syllable repetition of polysyllabic words, and the use of interjections, rephrasing, and repeated revisions. This situation is a common phenomenon in daily life and does not affect daily communication and emotional expression. Speech disfluency also occurs in children, but as children grow older and develop stronger expressive abilities, this disfluency tends to improve. Stuttering [3] is a language disorder characterized by symptoms such as speech disfluency, repetition or prolongation of syllables, pauses, and difficulty organizing language. The World Health Organization defines stuttering as a disorder of speech rhythm [4] in which the stutterer cannot express themselves clearly due to involuntary repetition, prolongation, or interruption of sounds. According to epidemiological survey data, there are currently over 70 million stuttering patients worldwide, with a far greater proportion of male patients than female patients [5; 6]. Therefore, stuttering is an important chronic speech disorder that deserves widespread attention. Therefore, if normal disfluency is present, it is necessary to comprehensively improve oral expression ability. Speakers may participate in speaking exams, such as the TOEFL iBT speaking test for English or the Mandarin Proficiency Test (Putonghua Shuiping Ceshi, PSC) for Chinese, to improve their oral abilities, where fluency is an important aspect of the exam. However, the assessment of fluency, especially for spontaneous speech expression tasks, is still manually scored, which is highly subjective and requires a significant amount of manpower. Furthermore, these assessments usually only return a corresponding fluency level or score without indicating specific disfluency features, making it difficult for speakers to receive timely feedback and make corrections.