## 5 Results and Analysis
### Evaluation on Limited Data  
Given the limited resources of the disfluent speech dataset, this study aims to train a more effective model using the limited disfluent speech data. Consequently, the dataset is divided, and the proposed disfluent speech detection model is compared on the complete SEP-28k dataset, 1/2 of the dataset, and 1/4 of the dataset, and the results are shown in Table 4. The data in Table 4 indicates that the \(F1\) of the proposed disfluency detection model decreases as the number of datasets decreases. However, with only 1/4 of the dataset, the \(F1\) for all disfluent features, except for the Bl feature, remains above 0.7. This indicates that the proposed model can effectively detect disfluencies even with limited data resources. Figure 11 visualizes the \(F1\) of different features under different dataset sizes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Disfluency** & **Dataset** & **Data Size in Minutes** & \(F1\) & _Precision_ & _Recall_ & _Accuracy (\%)_ \\ \hline \multirow{3}{*}{Snd} & 1/1 & 75 & 0.90 & 0.82 & 0.99 & 89.08 \\  & 1/2 & 37 & 0.81 & 0.74 & 0.89 & 78.88 \\  & 1/4 & 19 & 0.74 & 0.70 & 0.77 & 72.33 \\ \hline \multirow{3}{*}{WP} & 1/1 & 148 & 0.90 & 0.84 & 0.96 & 86.76 \\  & 1/2 & 74 & 0.79 & 0.78 & 0.81 & 76.36 \\  & 1/4 & 37 & 0.75 & 0.74 & 0.77 & 70.17 \\ \hline \multirow{3}{*}{Pro} & 1/1 & 75 & 0.89 & 0.82 & 0.98 & 88.11 \\  & 1/2 & 37 & 0.78 & 0.71 & 0.87 & 75.97 \\  & 1/4 & 19 & 0.72 & 0.68 & 0.77 & 70.39 \\ \hline \multirow{3}{*}{Intrj} & 1/1 & 248 & 0.84 & 0.87 & 0.81 & 83.82 \\  & 1/2 & 124 & 0.83 & 0.85 & 0.81 & 78.18 \\  & 1/4 & 62 & 0.80 & 0.84 & 0.76 & 76.90 \\ \hline \multirow{3}{*}{Bl} & 1/1 & 45 & 0.79 & 0.77 & 0.81 & 78.57 \\  & 1/2 & 22 & 0.72 & 0.68 & 0.78 & 70.41 \\ \cline{1-1}  & 1/4 & 11 & 0.69 & 0.64 & 0.73 & 66.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for all disfluencies on the SEP-28k dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Setting** \\ \hline Learning rate & \(1\times 10^{-4}\) \\ Batch size & 512 \\ Optimizer & Adam \\ Loss function & CrossEntropyLoss \\ Audio feature dimension & 768 \\ Attention dimension/number of heads & 50/10 \\ CNN hidden layer dimension & 50 \\ wav2 vec2.0 (Chinese) & TencentGameMate/chinese-wav2 vec2-base \\ wav2 vec2.0 (English) & facebook/wav2 vec2-base-960h \\ \hline \hline \end{tabular}
\end{table}
Table 3: The hyperparameters set in the experiments.  
In addition, this paper also conducts experiments on different disfluency features on the self-built PSC-PS-DF dataset, and the experimental results are shown in Table 5. The experimental results demonstrate that the proposed model also achieves excellent detection results in the Chinese disfluent dataset, with the BI, WP and Intrj features performing well in Chinese speech disfluency detection, demonstrating that blocks and repetitions as well as interjection features are more easily detectable types of disfluencies in spoken language. In contrast, the performance of the Pro feature is not as good as the other disfluent features, indicating that the prolongations feature of speech does not provide a clear distinction between fluent and disfluent speech.