# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Class-Balanced Learning

Table 11: Performance comparison of weighted batch sampling across multiple metrics for 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. The GeMAPS \(\oplus\) MFB configuration exhibits the most pronounced benefits from weighted sampling under restricted adaptation. Speaker count accuracy improves from 67.3% to 72.4%, while speaker turn detection shows substantial enhancement, with the F1-score increasing from 47.09% to 51.63%. However, this comes at the cost of increased missed speech (Miss rate increasing from 9.6% to 12.3%), indicating a potential trade-off between speaker discrimination and speech detection capabilities. Across all feature configurations, weighted sampling demonstrates a clear interaction with the adaptation strategy. When applied to the complete adaptation set (CH1 all), it generally leads to performance degradation, particularly affecting DER through increased missed speech. However, when combined with restricted adaptation (CH1 2-4 spk), it \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adapation**} & \multirow{2}{*}{**Strategy**} & \multicolumn{5}{c}{**DER Components (\%)**} & \multicolumn{5}{c}{**Speaker Turn Detection**} & \multicolumn{2}{c}{**Overlap Speech Detection**} \\ \cline{3-11}  & & **Miss** & **FA** & **Spk. Error** & **Total** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{CH1 all} & No Weight & 5.0 & 8.4 & 7.6 & 20.95 & 56.20 & 37.97 & 45.32 & 42.5 & 45.6 & 44.0 \\  & Weighted & 8.2 & 7.7 & 8.0 & 23.86 & 55.45 & 36.88 & 44.30 & 40.5 & 44.6 & 42.5 \\ \hline \multirow{2}{*}{CH1 2-4 spk} & No Weight & 5.3 & 6.3 & 5.5 & 17.09 & 59.84 & 40.54 & 48.33 & 45.2 & 47.0 & 46.1 \\  & Weighted & 5.3 & 6.2 & 5.3 & 16.87 & 59.91 & 40.56 & 48.37 & 45.5 & 47.5 & 46.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance comparison of weighted batch sampling across multiple metrics for ECAPAC-TDNN \(\oplus\) 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. Figure 8: Impact of weighted batch sampling on speaker counting performance across different feature configurations and adaptation strategies. Results compare standard sampling versus weighted sampling for both complete (CH1 all) and restricted (CH1 2–4 spk) adaptation sets. **Top**: Speaker Count Accuracy (%). **Bottom**: Speaker Count Error. shows potential benefits, especially for feature configurations that combine acoustic and speaker-specific information (ECAPA-TDNN and GeMAPS). This suggests that weighted sampling might be most effective when the adaptation data distribution is more controlled, allowing the model to learn from a balanced set of examples without being overwhelmed by the complexity of higher speaker counts. The effectiveness of Gaussian kernel-based label smoothing demonstrates a strong dependence on the temporal resolution used during adaptation, as illustrated in Figure 9. The interaction between these factors varies significantly across feature configurations, revealing important patterns in system behavior. The baseline MFB features (Table 14) show that while label smoothing alone offers limited benefits, its combination with reduced subsampling yields notable improvements. The DER decreases from 16.8% to 16.1%, with concurrent enhancements in speaker turn detection performance (F1-score increasing from 53.16% to 54.3%). The improvement manifests primarily through reduced missed speech (from 9.0% to 8.7%) and enhanced speaker boundary precision. The 250 ms collar is a key aspect in this context, as the label smoothing performed with either a subsampling factor of 10 or 5 remains within the margin covered by the collar during inference.