###### Abstract
If stuttering is present, it needs to be taken seriously, and the individual should be trained to reshape fluency to reduce the impact of stuttering on their life and psychology. Speech-language pathologists diagnose stutteres and manually measure their fluency, calculate the incidence of disfluency, and evaluate the stutterer's response throughout the entire treatment process. However, assessments of stuttering of this nature may be subjective, lack consistency, and be susceptible to errors. This paper suggests that an automatic method is needed to detect speech disfluencies, which can help people overcome speaking difficulties, improve their language expression abilities, and assist therapists in tracking the progress of stuttering patients. Existing automated methods for detecting speech disfluency require large-scale disfluency-labeled speech data during training and are designed for specific languages, making it difficult to extend their use to the detection of speech disfluency in other languages. Additionally, in practical detection scenarios, the length of disfluent speech is not fixed. However, most existing research on fixed-length speech disfluency detection has overlooked the detection of variable-length speech disfluency. In recent studies, researchers have started to consider building disfluency detection models with limited data and have attempted to use pre-training and fine-tuning models for disfluency detection. However, these methods still have many limitations. For example, the latest models are still designed and trained for specific languages and dialects, making it difficult to detect disfluencies in other languages or dialects. Moreover, these models may not generalize well to other datasets or contexts. Additionally, many of the latest models are based on fixed-length modeling, which may not be suitable for detecting disfluencies of varying lengths. Furthermore, many of the latest studies still adopt simple machine learning methods, although the use of deep learning methods may potentially achieve better results in disfluency detection given the recent advances in deep learning in the field of speech. In summary, while there have been some recent advances in automatic speech disfluency detection, there are still several limitations and challenges that need to be addressed. To address these issues, this study proposes a disfluency detection method designed for limited disfluent speech data. The method extracts disfluency features from the wav2 vec2.0 model for disfluency detection. In the model construction process, the entropy invariance of attention mechanisms is used to enable the model to generalize to disfluent speech of different lengths, allowing for variable-lengthspeech disfluency detection. Finally, this paper conducts experiments on both an English open-source database and a self-built database for Chinese and finds that the model can be applied in different language environments with strong scalability. In summary, the main contributions of this paper can be summarized as follows:  
* This paper addresses the shortage of Chinese disfluent speech data by creating the PSC-PS-DF dataset, which includes four disfluent features: interjections, blocks, prolongations, and repetitions. * A classification network is developed in this paper for automated speech disfluency detection by combining CNN and Transformer and utilizing context embeddings from the pre-training model, wav2 vec2.0. The network outperforms the baseline model in terms of detection accuracy and training time, even when trained with limited data. * Considering that the length of disfluent speech data varies in practical detection scenarios, this paper improves the model based on the entropy invariance of attention mechanisms, allowing the model's results to generalize to speech data of different lengths, which means that even if the training and testing data have different disfluent data lengths, the model can still achieve good results.