## 4 Results  
### Regression Analysis  
We employed a logistic regression model to systematically analyze the significance and impact of various features on turn-taking success. Specifically, the outcome of turn-taking (categorized as \"obstructed\" or \"unobstructed\" ) was set as the dependent variable, while interaction dynamics features were used as independent variables. Prior to model construction, all feature data were standardized, and the variance inflation factors (VIF) for all independent variables were verified to be below 5, indicating no significant multi-collinearity issues. The regression analysis results are presented in Table 3. The model's McFadden pseudo-\(R^{2}\) value is 0.192, close to 0.2, indicating that the model effectively captures key relationships between the independent and dependent variables.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Features** & **Mean** & **Std** & **Min** & **Median** & **Max** \\ \hline Neg Log Speaking Interval & \(-\)2.563 & 1.463 & \(-\)5.710 & \(-\)2.617 & 2.900 \\ Group Area & 15.258 & 7.403 & 0.768 & 17.067 & 32.755 \\ Speaking Duration Ratio & 0.476 & 0.364 & 0.000 & 0.481 & 1.000 \\ Utterance Count & 1.049 & 0.841 & 0.000 & 1.000 & 5.000 \\ Speaking Intention Count & 0.169 & 0.396 & 0.000 & 0.000 & 2.000 \\ Max Speaker Duration Ratio & 0.729 & 0.416 & 0.000 & 1.000 & 1.000 \\ Speaking Overlap Ratio & 0.043 & 0.138 & 0.000 & 0.000 & 0.889 \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive Statistics of Features.  
Figure 5: Example of analysis window selection for tags. dark gray rectangles represent unsuccessful speaking intention tags, while light gray rectangles represent successful speaking intention tags. t denotes 1.5 s. UT indicates utterances. The third one demonstrates the case of window shifting.  
From the regression coefficients (\(\beta\) values) and their significance (\(p\)-values), the group area (\(\beta=-0.0852\), \(p=0.429\)) and max speaker duration ratio (\(\beta=0.3258\), \(p=0.051\)) were found to have no significant effect on turn-taking success. However, the utterance count (\(\beta=-0.5211\), \(p=0.006\)) and speaking duration ratio (\(\beta=-0.9142\), \(p=0.000\)) exhibited significant negative effects, indicating that high-frequency or continuous speaking scenarios increase the difficulty for participants to successfully acquire speaking turns.  
Additionally, the feature negative logarithm of speaking interval (\(\beta=0.2710\), \(p=0.014\)) positively influenced turn-taking success, specifically indicating that shorter intervals since their last turn made their speaking intentions more likely to be acknowledged. On the other hand, the speaking intention count (\(\beta=-0.5344\), \(p=0.000\)) had a significant negative impact on coordination outcomes, meaning that when speaking intentions occurred in clusters, the difficulty of turn-taking coordination increased accordingly.  
An unexpected finding was that speaking overlap ratio (\(\beta=0.3875\), \(p=0.009\)) had a positive effect on successful turn-taking, suggesting that in scenarios with a higher proportion of overlapping speech, the failure rate of turn-taking decreased. This phenomenon may indicate that participants actively coordinate turn-taking during overlaps to avoid prolonged speech conflicts.  
To further validate the predictive performance of the significant features, we retrained the logistic regression model using these features to predict turn-taking failures. The dataset was divided into 90% for training and 10% for testing, with the test set containing 50 samples (24 positive cases and 26 negative cases). On the test set, we achieved an overall accuracy of 82%, with an AUC value of 0.83. This indicates that the model has high accuracy in predicting failures and demonstrates strong discriminative ability. These results further support the critical role of these features in turn-taking coordination and confirm their potential for application and effective prediction in new datasets.