## 2 Related Work
### Disfluency Detection with Deep Learning  
With the promising progress of deep learning in speech-related fields, such as speech recognition and emotion classification, the application of DL in disfluency detection has also increased in recent years. [PERSON] et al. [47] used a bidirectional long short-term memory neural network (BLSTM) model for speech disfluency detection in 2016. The model not only used word sequences but also included pattern matching features as inputs and used integer linear programming in the final output to combine the constraints of the network structure, achieving advanced performance. In their 2019 work on the same dataset, [PERSON] et al. [48] improved the speech disfluency detection model by using BLSTM with prosodic cues and achieved better classification results. In the same year, [PERSON] et al. [49] applied BLSTM and attention mechanisms to weight each frame based on its importance rather than directly measuring the overall information from the speech, resulting in more accurate classification. [PERSON] et al. [29] used spectrograms as features and achieved high accuracy in speech disfluency classification with a BLSTM model. [PERSON] et al. [50] proposed a method that combines multiple self-supervised tasks and fine-tunes a pre-trained network using labeled disfluency detection training data, achieving good detection results with limited data. [PERSON] et al. [51] proposed a controllable time-delay Transformer (CT-Transformer) model that jointly performs punctuation prediction and disfluency detection tasks. The experimental results showed that the method outperformed existing state-of-the-art models in terms of F-score and achieved a competitive inference speed. [PERSON] et al. [24] proposed a novel deep learning-based stutter detection model, StutterNet, which uses time-delay neural network (TDNN) to capture disfluent speech in a contextual aspect and significantly reduces the number of training parameters while obtaining reliable results. [PERSON] et al. [35] constructed a DisfluencyNet network based on contextual embeddings of the wav2 vec2.0 model. The main building blocks of this network are convolution layers with max-pooling and fully connected layers. The experiment showed that this method trained a powerful network within just a few minutes of data and achieved excellent disfluency detection results. [PERSON] et al. [31] proposed a new stutter event detection model based on log melspectrograms and a 2D atrous CNN. The experimental results showed that the model outperformed the state-of-the-art methods in prolongation. In general, deep learning models have better performance than traditional machine learning models because they can directly learn features from raw speech signals in an end-to-endmanner and can use more complex models to model the time and frequency information in speech signals, resulting in more accurate training of speech disfluency detection classifiers.  
In summary, most of the existing work on disfluency detection in speech focuses on English datasets, and few studies have validated the effectiveness of the models on datasets in different languages. Additionally, the existing work mostly includes experiments on large-scale datasets and is only suitable for detecting disfluencies of fixed lengths, while the length of disfluent speech data is not fixed. Therefore, there is a lack of a disfluency detection model that is applicable to different languages and variable-length disfluent speech data. In this paper, we have constructed a disfluency detection model combining wav2 vec2.0, a CNN, and a Transformer, which is inspired by [35]. The model can achieve good results in different language scenarios with minute-level data training, and the entropy invariance of attention mechanisms is employed to enable the model to generalize to disfluent speech of different lengths, making it more applicable to real-world disfluency detection scenarios.