# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Class-Balanced Learning  
To address the inherent class imbalance of the number of speakers in the adaptation set, where conversations with higher speaker counts are underrepresented, we implemented a weighted batch sampling strategy. This approach adjusts the sampling probability inversely proportional to the class frequency, aiming to provide more balanced exposure to different conversation configurations during adaptation. Figure 8 illustrates the impact of weighted sampling on speaker counting performance. The results reveal distinct patterns across feature configurations. For the baseline MFB features with complete adaptation (CH1 all), weighted sampling maintains comparable speaker counting performance, with accuracy decreasing marginally from 73.6% to 72.4%. However, when applied to restricted adaptation, it leads to a more notable degradation in accuracy (74.4% to 70.4%), suggesting that the weighted approach might disrupt the learning of speaker characteristics when the adaptation set is more homogeneous. The ECAPA-TDNN \(\oplus\) MFB configuration demonstrates contrasting behavior between adaptation strategies. With complete adaptation, weighted sampling significantly degrades speaker count accuracy from 64.0% to 56.3%. Conversely, under restricted adaptation, it shows remarkable stability, maintaining performance (68.4% to 69.6%) while improving SCE from 0.376 to 0.360. This suggests that the effectiveness of weighted sampling for this feature combination is highly dependent on the adaptation data distribution. As shown in Tables 11-13, the impact of weighted sampling extends beyond speaker counting metrics. For MFB features with restricted adaptation, while speaker counting performance decreases, there is a notable improvement in speaker turn detection precision (62.63% to 62.24%) and recall (47.51% to 46.98%). This suggests that despite the apparent degradation in speaker counting, the model might be learning more robust speaker-specific characteristics. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adaptation**} & \multirow{2}{*}{**Dropout**} & \multicolumn{3}{c}{**Speaker Count**} & \multicolumn{3}{c}{**Speaker Turn Detection**} & \multicolumn{3}{c}{**Overlap Speech Detection**} \\ \cline{3-10}  & & **SCE** & **Acc.** & **P** & **R** & **F1** & **P** & **R** & **F1** & **Acc.** \\ \hline \multirow{3}{*}{CH1 all} & 0.1 & 0.428 & 66.4 & 54.58 & 43.17 & 48.21 & 55.8 & 36.3 & 44.0 & 86.17 \\  & 0.3 & 0.524 & 59.2 & 56.77 & 43.51 & 49.26 & 38.4 & 33.6 & 35.9 & 82.02 \\  & 0.5 & 0.636 & 50.0 & 51.58 & 38.21 & 43.90 & 53.0 & 15.1 & 23.4 & 85.30 \\ \hline \multirow{3}{*}{CH1 2–4 spk} & 0.1 & 0.415 & 67.3 & 53.40 & 42.12 & 47.09 & 56.2 & 37.9 & 45.3 & 86.29 \\  & 0.3 & 0.328 & 73.2 & 60.63 & 47.08 & 53.00 & 66.2 & 29.9 & 41.2 & 87.22 \\ \cline{1-1}  & 0.5 & 0.396 & 65.2 & 55.74 & 45.97 & 50.39 & 61.0 & 21.6 & 31.9 & 86.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison of different dropout rates across multiple metrics for GeMAPS \(\oplus\) MFB features. Results are shown for both complete (CH1 all) and restricted (CH1 2–4 spk) adaptation sets. P indicates Precision and R indicates Recall. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adaptation**} & \multirow{2}{*}{**Strategy**} & \multicolumn{3}{c}{**DER Components (\%)**} & \multicolumn{3}{c}{**Speaker Turn Detection**} & \multicolumn{3}{c}{**Overlap Speech Detection**} \\ \cline{3-10}  & & **Miss** & **FA** & **Spk. Error** & **Total** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{3}{*}{CH1 all} & No Weight & 9.0 & 2.6 & 5.2 & 16.8 & 62.41 & 46.31 & 53.16 & 64.9 & 42.4 & 51.3 \\  & Weighted & 7.7 & 3.4 & 5.8 & 16.93 & 57.85 & 45.30 & 50.81 & 62.0 & 42.7 & 50.6 \\ \hline \multirow{3}{*}{CH1 2–4 spk} & No Weight & 8.0 & 2.7 & 4.9 & 15.64 & 62.63 & 47.51 & 53.97 & 64.4 & 44.5 & 52.6 \\  & Weighted & 11.1 & 2.2 & 5.8 & 19.08 & 62.24 & 46.98 & 53.51 & 65.1 & 31.4 & 42.3 \\ \hline \hline \end{tabular}
\end{table}