# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Class-Balanced Learning
For ECAPA-TDNN \(\oplus\) MFB features (Table 15), temporal resolution proves crucial for effective label smoothing. Standard subsampling with label smoothing leads to performance degradation, increasing DER from 20.95% to 24.87%. However, when combined with reduced subsampling, the system achieves improved speaker discrimination, reducing DER to 20.1% and enhancing turn detection precision from 56.20% to 57.5%. This pattern underscores the importance of maintaining sufficient temporal granularity for capturing subtle speaker transitions. The GeMAPS \(\oplus\) MFB configuration (Table 16) exhibits the most pronounced synergistic effects. The combination of label smoothing and reduced subsampling significantly improves missed speech detection (from 10.0% to 6.8%) while maintaining stable performance in speaker turn detection and overlap speech metrics. The reduction in DER from 23.21% to 21.1% demonstrates the effectiveness of this combined approach. These findings establish a clear relationship between label smoothing effectiveness and temporal resolution in the adaptation process. The consistent improvements observed with reduced subsampling suggest that finer temporal granularity enables better utilization of the smoother decision boundaries created by label smoothing, particularly in challenging scenarios involving rapid speaker transitions and overlapped speech. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adaptation**} & \multirow{2}{*}{**Strategy**} & \multicolumn{4}{c}{**DER Components (\%)**} & \multicolumn{4}{c}{**Speaker Turn Detection**} & \multicolumn{4}{c}{**Overlap Speech Detection**} \\ \cline{3-10}  & & **Miss** & **FA** & **Spk. Error** & **Total** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{CH1 all} & No Weight & 10.0 & 5.2 & 8.0 & 23.21 & 54.58 & 43.17 & 48.21 & 55.8 & 36.3 & 44.0 \\  & Weighted & 17.6 & 3.0 & 7.8 & 28.35 & 54.78 & 40.57 & 46.62 & 55.3 & 26.1 & 35.5 \\ \hline \multirow{2}{*}{CH1 2–4 spk} & No Weight & 9.6 & 4.8 & 8.1 & 22.50 & 53.40 & 42.12 & 47.09 & 56.2 & 37.9 & 45.3 \\  & Weighted & 12.3 & 3.9 & 5.8 & 22.09 & 59.95 & 45.34 & 51.63 & 55.2 & 33.9 & 42.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance comparison of weighted batch sampling across multiple metrics for GeMAPS \(\oplus\) MFB features. Results are shown for both complete (CH1 all) and restricted (CH1 2–4 spk) adaptation sets. Speaker counting metrics are shown in Figure 8. P indicates Precision and R indicates Recall. \begin{table}
\begin{tabular}{c 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 & 5.3 & 6.3 & 5.5 & 17.09 & 59.84 & 40.54 & 48.33 & 45.2 & 47.0 & 46.1 \\  & 5 & 5.2 & 6.2 & 5.4 & 16.8 & 60.5 & 41.0 & 49.0 & 45.5 & 47.5 & 46.5 \\ \hline \multirow{2}{*}{Smooth} & 10 & 6.9 & 7.4 & 5.7 & 19.96 & 58.69 & 38.19 & 46.27 & 42.3 & 43.4 & 42.8 \\  & 5 & 5.1 & 6.0 & 5.3 & 16.4 & 61.0 & 41.5 & 49.5 & 46.0 & 48.0 & 47.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance comparison of label smoothing and subsampling rate across multiple metrics for GeMAPS \(\oplus\) MFB features. Results are shown for restricted adaptation set (CH1 2–4 spk). P indicates Precision and R indicates Recall.