# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Class-Balanced Learning
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Subsampling**} & \multicolumn{6}{c}{**DER Components (\%)**} & \multicolumn{6}{c}{**Speaker Turn Detection**} & \multicolumn{6}{c}{**Overlap Speech Detection**} \\ \cline{3-13}  & & **Miss** & **FA** & **Spk. Error** & **Total** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{No Smooth} & 10 & 8.0 & 2.7 & 4.9 & 15.64 & 62.63 & 47.51 & 53.97 & 64.4 & 44.5 & 52.6 \\  & 5 & 8.3 & 2.6 & 5.1 & 16.0 & 63.5 & 48.0 & 54.7 & 64.8 & 45.0 & 53.0 \\ \hline \multirow{2}{*}{Smooth} & 10 & 7.8 & 4.2 & 5.4 & 17.39 & 60.60 & 44.70 & 51.44 & 56.0 & 47.0 & 51.4 \\  & 5 & 8.0 & 2.8 & 5.7 & 16.43 & 62.49 & 46.97 & 53.64 & 63.1 & 44.9 & 52.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance comparison of label smoothing and subsampling rate across multiple metrics for MFB features. Results are shown for restricted adaptation set (CH1 2–4 spk). P indicates Precision and R indicates Recall. Figure 9: Impact of label smoothing and subsampling rate applied during adaptation on system performance across different feature configurations. Top row shows DER (%) variations; bottom row shows Speaker Turn Detection F1-score changes. Each column represents a different feature configuration. Adjusting Subsampling Rate  
To systematically investigate the impact of temporal resolution on diarization performance, we conducted experiments reducing the subsampling factor from the conventional 10 to 5 across all feature configurations. This modification effectively doubles the temporal resolution during adaptation, potentially allowing the model to capture more precise speaker transitions and overlapped speech patterns. Figure 10 illustrates the performance impact of enhanced temporal resolution across feature configurations, with all systems using consistent regularization (dropout = 0.3) and adaptation strategy (CH1 2-4 spk). For the baseline MEB features, temporal resolution enhancement yields modest improvements, with DER decreasing from 16.17% to 15.76%, a relative improvement of 2.5%. The ECAPA-TDNN \(\oplus\) MFB configuration demonstrates more substantial gains, with DER decreasing from 15.8% to 14.89%, a relative improvement of 5.8%. Similarly, the GeMAPS \(\oplus\) MFB configuration shows a 4.4% relative improvement, with DER decreasing from 19.74% to 18.87%. The consistent DER improvement across all configurations suggests that enhanced temporal resolution provides inherent benefits to the diarization process. Notably, the ECAPA-TDNN \(\oplus\) MFB configuration achieves the lowest overall DER of 14.89% in our experiments, establishing a new performance benchmark. This result underscores the importance of temporal resolution as a key factor in adaptation strategy optimization. The impact of enhanced temporal resolution on speaker turn detection performance varies across configurations. While the MFB features show minimal change in F1-score (55.26% to 55.14%), the ECAPA-TDNN \(\oplus\) MFB configuration demonstrates a more substantial improvement from 48.89% to 50.53%. This 3.4% relative enhancement in boundary detection precision is particularly significant as it indicates improved capability to identify precise speaker transitions, a critical aspect of diarization quality that might be obscured in the overall DER metric. Table 17 presents a detailed analysis of error components for systems with enhanced temporal resolution. The ECAPA-TDNN \(\oplus\) MFB configuration demonstrates a well-balanced error distribution, with missed speech at 5.7%, false alarms at 3.7%, and speaker confusion error at 5.5%. In contrast, the GeMAPS \(\oplus\) MFB configuratio  
Figure 10: Performance gains from enhanced temporal resolution across feature configurations, with consistent regularization (dropout = 0.3) and adaptation strategy (CH1 2–4 spk). **Left**: Diarization Error Rate (DER), where lower values indicate better performance.