# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.2 Feature Performance in Multi-Speaker Scenarios  
We evaluated the performance of our proposed feature combinations: ECAPA-TDNN \(\oplus\) MFB and GeMAPS \(\oplus\) MFB across multi-speaker scenarios. Table 6 presents the DER results for both configurations compared to our baseline implementation.  
To provide a more comprehensive analysis of system behavior, we evaluated additional performance metrics for each configuration, as shown in Table 7.  
The comprehensive metrics analysis reveals interesting patterns across configurations. The baseline MFB features demonstrate superior performance in several aspects, particularly in speaker counting with the highest accuracy (73.20%) and lowest SCE (0.348). This advantage extends to speaker turn detection, where MFB achieves the best F1-score of 53.16%, outperforming both feature combinations. In terms of overlap speech detection, MFB maintains its edge with the highest F1-score (51.30%) and accuracy (87.95%).  
When comparing the feature combinations, GeMAPS \(\oplus\) MFB shows slightly better speaker counting performance than ECAPA-TDNN \(\oplus\) MFB, with a lower SCE (0.428 vs. 0.444) and higher accuracy (66.40% vs. 64.00%). It also demonstrates more balanced performance in speaker turn detection, achieving a higher F1-score of 48.21% compared to 45.32% for ECAPA-TDNN \(\oplus\) MFB. In terms of overlap speech detection, while both combinations achieve the same F1-score of 44.00%, they do so through different  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Training Set** & **All** & **2 spk** & **3 spk** & **4 spk** & **5 spk** & **6 spk** \\ \hline \multirow{4}{*}{Baseline (ours)} & SC 2 spkr & 22.24 & 10.33 & 22.71 & 30.64 & 49.18 & 48.65 \\  & SC 1–4 spkr & 18.28 & 9.91 & 16.47 & 24.29 & 45.88 & 46.35 \\  & CH1 all & 16.8 & 7.99 & 15.42 & 23.47 & 37.17 & 41.07 \\ \hline \multirow{4}{*}{ECAPA-TDNN \(\oplus\) MFB} & SC 2 spkr & 26.66 & 12.31 & 26.12 & 38.41 & 44.22 & 57.37 \\  & SC 1–4 spkr & 27.41 & 14.6 & 24.24 & 41.09 & 52.91 & 54.82 \\  & CH1 all & 20.95 & 10.45 & 19.00 & 31.79 & 38.44 & 51.32 \\ \hline \multirow{4}{*}{GeMAPS \(\oplus\) MFB} & SC 2 spkr & 24.90 & 10.75 & 27.45 & 36.39 & 48.50 & 54.02 \\  & SC 1–4 spkr & 22.91 & 11.01 & 23.99 & 30.56 & 45.23 & 49.15 \\ \cline{1-1}  & CH1 all & 23.21 & 14.34 & 22.49 & 29.38 & 42.27 & 49.44 \\ \hline \hline \end{tabular}
\end{table}
Table 6: DER (%) performance comparison on CH2 evaluation set across different feature configurations at training stage. Results are broken down by the number of speakers in each recording.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **Metric** & **MFB** & **ECAPA-TDNN \(\oplus\) MFB** & **GeMAPS \(\oplus\) MFB** \\ \hline \multirow{2}{*}{Speaker Count} & Accuracy (\%) & 73.20 & 64.00 & 66.40 \\  & SCE & 0.348 & 0.444 & 0.428 \\ \hline \multirow{4}{*}{Speaker Turn Det.} & Precision (\%) & 62.41 & 56.20 & 54.58 \\  & Recall (\%) & 46.31 & 37.97 & 43.17 \\  & F1-score (\%) & 53.16 & 45.32 & 48.21 \\ \hline \multirow{4}{*}{Overlap Speech Det.} & Precision (\%) & 64.90 & 42.50 & 55.80 \\  & Recall (\%) & 42.40 & 45.60 & 36.30 \\ \cline{1-1}  & F1-score (\%) & 51.30 & 44.00 & 44.00 \\ \cline{1-1}  & Accuracy (\%) & 87.95 & 82.64 & 86.17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comprehensive performance metrics comparison across different feature configurations on the CH2 evaluation set after CH1 adaptation. Results show that while feature combinations improve certain aspects of performance, they introduce different trade-offs in speaker discrimination capabilities.  
precision-recall trade-offs, with GeMAPS \(\oplus\) MFB showing notably higher precision but lower recall compared to ECAPA-TDNN \(\oplus\) MFB.