## 5 Results and Analysis
### Length-Scaled Attention  
This paper presents length-scaled attention as a solution to the problem of fixed training sequence length and varying testing sequence length. In this method, the training and testing sequences are standardized to a length of 3 s for standard input. To evaluate the effectiveness of length-scaled attention, we tested the method by clipping the original sequence to half its original length. Then, we conducted experiments to verify the impact of length-scaled attention on the experiments while keeping all other configurations unchanged. Tables 10 and 11, respectively, show the changes in model performance when the length of training and testing sequences varies in different datasets. When trained and tested on data of the same length, adding length-scaled attention leads to an improve \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disfluency** & **Model** & **F1** & _Precision_ & _Recall_ & _Accuracy (\%)_ \\ \hline \multirow{4}{*}{Snd} & w/o CNN & 0.69 & 0.69 & 0.69 & 68.69 \\  & w/o & 0.66 & 0.68 & 0.65 & 67.23 \\  & Transformer & 0.74 & 0.70 & 0.77 & 72.33 \\ \hline \multirow{4}{*}{WP} & w/o CNN & 0.71 & 0.66 & 0.76 & 69.18 \\  & w/o & 0.70 & 0.68 & 0.71 & 68.31 \\  & Transformer & 0.75 & 0.74 & 0.77 & 70.17 \\ \hline \multirow{4}{*}{Pro} & w/o CNN & 0.68 & 0.63 & 0.73 & 65.53 \\  & w/o & 0.62 & 0.61 & 0.63 & 61.65 \\  & Transformer & 0.72 & 0.68 & 0.77 & 70.39 \\ \hline \multirow{4}{*}{Intrj} & w/o CNN & 0.77 & 0.83 & 0.71 & 74.91 \\  & w/o & 0.67 & 0.71 & 0.63 & 71.45 \\  & Transformer & 0.80 & 0.84 & 0.76 & 76.90 \\ \hline \multirow{4}{*}{Bl} & w/o CNN & 0.61 & 0.56 & 0.67 & 57.14 \\  & w/o & 0.56 & 0.56 & 0.55 & 56.12 \\ \cline{1-1}  & Ours & 0.69 & 0.64 & 0.73 & 66.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the ablation experiment on the SEP-28k dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disfluency** & **Model** & **F1** & _Precision_ & _Recall_ & _Accuracy (\%)_ \\ \hline \multirow{4}{*}{WP} & w/o CNN & 0.98 & 0.98 & 0.98 & 97.81 \\  & w/o & 0.97 & 0.98 & 0.97 & 97.50 \\  & Transformer & 0.98 & 0.99 & 0.98 & 98.44 \\ \hline \multirow{4}{*}{Pro} & w/o CNN & 0.84 & 0.73 & 0.98 & 80.73 \\  & w/o & 0.82 & 0.70 & 0.97 & 77.98 \\  & Ours & 0.84 & 0.73 & 0.98 & 81.73 \\ \hline \multirow{4}{*}{Intrj} & w/o CNN & 0.94 & 0.96 & 0.92 & 93.84 \\  & w/o & 0.94 & 0.97 & 0.90 & 93.84 \\ \cline{1-1}  & Transformer & 0.94 & 0.97 & 0.92 & 94.52 \\ \hline \multirow{4}{*}{Bl} & w/o CNN & 0.95 & 0.96 & 0.94 & 95.00 \\ \cline{1-1}  & w/o & 0.97 & 0.96 & 0.97 & 96.67 \\ \cline{1-1}  & Transformer & 0.97 & 0.99 & 0.99 & 98.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of the ablation experiment on the PSC-PS-DF dataset. ment in experimental results, demonstrating the importance of length-scaled attention in disfluency detection models. In the SEP-28k dataset, WP and Intrj labels are enhanced by 0.14 and 0.03, respectively, using length-scaled attention compared to removing length-scaled attention for the same test length, while the training data length is reduced to half of the original length. In the PSC-PS-DF dataset, when the training data are half of their original length, compared to removing length-scaled attention, WP, Pro, and Intrj labels are enhanced by 0.03, 0.01, and 0.01, respectively, using length-scaled attention. Experimental results for both datasets show that the model without the addition of length-scaled attention performs worse in most disfluent label detection experiments when the training sequence length is reduced to half of the original length. Furthermore, as shown in Tables 10 and 11, shorter testing sequences lead to a decrease in the performance of length-scaled attention. Models that use length-scaled attention do not show their advantages, possibly because the duration of disfluency speech varies, and to standardize the length, zeros are sometimes padded at the end of the sequence. These clipped data may consist of useless zero frames.