## 6 Conclusions  
Speech is the most efficient and widely used means of communication among humans. However, not everyone has normal communication abilities, and speech disfluency often occurs during communication. Speech disfluency can manifest as normal speech disfluency in daily life for the general population, which is related to people's emotions, urgency, or nervousness. It can also manifest as stuttering in individuals who involuntarily repeat, prolong, or interrupt sounds, making it difficult for them to express themselves clearly. Currently, detecting speech disfluency in daily life and stuttering detection both require the assistance of experts, and there is a lack of validated automated evaluation methods. The current methods for detecting speech disfluency heavily depend on annotated data, which can be expensive. Furthermore, these methods do not address the issue of variable-length disfluent speech, which restricts the scalability of detection methods. To address these limitations, this paper proposes a method for detecting speech disfluency using wav2 vec2.0, CNN, and Transformer. The main feature of this method is its applicability to different languages and its ability to handle variable-length disfluent speech signals. Firstly, this paper constructs the PSC-PS-DF dataset for Chinese disfluent speech, which consists of four disfluent features: interjections, blocks, prolongations, and repetitions. Then, the paper uses context embeddings from the pre-training model wav2 vec2.0 and combines CNN and Transformer to build a classification network for automated speech disfluency detection. Considering that the length of disfluent speech data varies in practical detection scenarios, the 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. In the experiments, we tested the proposed method using speech signals of different languages and lengths, and the results showed that the proposed method performed well in these scenarios. The paper concludes that the proposed model achieved good disfluency detection results in both self-built datasets and the open-source dataset for English disfluent speech, indicating its potential for detecting speech disfluency in different languages and different lengths. Our method can be applied to different fields such as speech recognition, speech synthesis, and human-computer interactions. In future research, we hope to improve the proposed speech disfluency detection model in various ways, as follows:  
* Since the accuracy of speech disfluency detection methods is directly related to the size of the data, but the cost of collecting and labeling disfluent data is high, we can use more efficient data processing and extraction methods to obtain more reliable disfluency detection results with limited data. * The wav2 vec2.0 model has a good effect on extracting disfluent speech features. In this paper, we only used the context representation of the last hidden layer of the wav2 vec2.0 model as the input to the model. In future research, we can consider fine-tuning the wav2 vec2.0 model to obtain effective detection results. In addition, we can also try using other speech pre-training models such as HuBERT and wavLM instead of the wav2 vec2.0 model used in this paper to obtain better results. * This paper conduct experiments on self-built and open-source English datasets, including SEP-28k, to verify that the model can be applied in different language environments. In the future, we can conduct experiments on this model in more languages to verify its reliability. * In the future, we can expand our work from single-task scenarios to multi-classification scenarios, not only detecting disfluency but also distinguishing between different types of speech disfluency events. Conceptualization, J.L. and A.W. ; methodology, J.L. and A.W. ; software, J.L. ; validation, J.L., D.W. and S.G.; formal analysis, J.L. ; investigation, J.L. ; resources, J.L. and S.G.; data curation, J.L.