## 5 Results and Analysis
### Comparison with Baseline Models  
In this section, the proposed model is compared with the long short-term memory (LSTM) model, MLP model, and the baseline model DisfluencyNet [35] on the 1/4 SEP-28k dataset, and the results are shown in Table 6. The results indicate that the proposed model in this paper yields superior detection results for the Snd, WP, Intrj, and Bl disfluency features when compared to other baseline models. Regarding the Pro disfluency feature, the proposed model in this paper achieves an F1-score that is 0.01 lower than that of the baseline model DisfluencyNet. This suggests that there is still scope for improving the detection of disfluencies such as prolongations. For a clearer comparison of how different baseline models perform on 1/4 of the SEP-28k dataset, please refer to Figure 12.  
In addition, this study also compares the convergence speed of the proposed model with the baseline model DisfluencyNet during the training process. Figures 13-16 show the training loss curves for the Snd, WP, Pro, and Bl labels at 400 epochs in the full  
Figure 11: The F1-score of all disfluencies on the SEP-28k dataset at different data sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disfluency** & **Data Size in Minutes** & **F1** & _Precision_ & _Recall_ & _Accuracy (\%)_ \\ \hline WP & 48 & 0.98 & 0.99 & 0.98 & 98.44 \\ Pro & 29 & 0.84 & 0.73 & 0.98 & 81.73 \\ Intrj & 22 & 0.94 & 0.97 & 0.92 & 94.52 \\ Bl & 54 & 0.99 & 0.99 & 0.99 & 98.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for all disfluencies on the PSC-PS-DF dataset.  
SEP-28k dataset, respectively, from which it can be seen that the proposed model converges significantly faster than the baseline model DisfluencyNet.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disfluency** & **Model** & **F1** & _Precision_ & _Recall_ & _Accuracy (\%)_ \\ \hline \multirow{4}{*}{Snd} & LSTM & 0.66 & 0.65 & 0.67 & 65.05 \\  & MLP & 0.70 & 0.68 & 0.72 & 69.17 \\  & DisfluencyNet & 0.72 & 0.67 & 0.79 & 70.00 \\  & Ours & 0.74 & 0.70 & 0.77 & 72.33 \\ \hline \multirow{4}{*}{WP} & LSTM & 0.72 & 0.71 & 0.74 & 67.70 \\  & MLP & 0.71 & 0.72 & 0.69 & 69.68 \\  & DisfluencyNet & 0.71 & 0.75 & 0.66 & 71.00 \\  & Ours & 0.75 & 0.74 & 0.77 & 70.17 \\ \hline \multirow{4}{*}{Pro} & LSTM & 0.60 & 0.64 & 0.60 & 60.44 \\  & MLP & 0.63 & 0.62 & 0.65 & 62.14 \\  & DisfluencyNet & 0.73 & 0.80 & 0.76 & 75.70 \\  & Ours & 0.72 & 0.68 & 0.77 & 70.39 \\ \hline \multirow{4}{*}{Intrj} & LSTM & 0.69 & 0.67 & 0.71 & 70.00 \\  & MLP & 0.68 & 0.81 & 0.59 & 72.73 \\  & DisfluencyNet & 0.79 & 0.79 & 0.79 & 74.50 \\  & Ours & 0.80 & 0.84 & 0.76 & 76.90 \\ \hline \multirow{4}{*}{Bl} & LSTM & 0.49 & 0.50 & 0.49 & 50.00 \\  & MLP & 0.56 & 0.53 & 0.59 & 53.06 \\  & DisfluencyNet & 0.58 & 0.54 & 0.61 & 55.00 \\  & Ours & 0.69 & 0.64 & 0.73 & 66.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of baseline models on 1/4 of the SEP-28k dataset.  
Figure 12: Performance of baseline models on 1/4 of the SEP-28k dataset.  
Figure 14: Training Loss Curves for WP.  
Figure 13: Training Loss Curves for Snd.  
Figure 15: Training Loss Curves for Pro.  
In this study, comparison experiments with different baseline models were also conducted on the self-built PSC-PS-DF dataset, and the results are shown in Table 7. The findings indicate that the model proposed in this paper outperforms other baseline models in detecting Pro, Intrj, and Bl labels in Chinese. However, there is no significant difference between the proposed model and other models for WP labels. The performance of the different models on the PSC-PS-DF dataset can be seen in Figure 17.