# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.1 Baseline Comparison  
To establish a solid foundation for our feature analysis in multi-speaker scenarios, we first replicated the baseline EEND-EDA system following the three-stage training  
Figure 5: Performance comparison of different GeMAPS subsets and context sizes on the CH2 2-spk set, with adaptation on CH1 2-spk set. Three feature configurations are compared: Complete GeMAPS, F0 + Formants, and a Reduced Set, across varying context sizes from 0 to 8. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Configuration** &
\begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} & **DER (\%)** & **\(\Delta\)DER(\%)** \\ \hline Complete GeMAPS & 62 & 13.60 & - \\ F0 + Formants & 24 & 45.33 & +31.73\% \\ Proposed Reduced Set & 52 & 13.93 & +0.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different GeMAPS feature configurations on CH2-2 spk. The Proposed Reduced Set excludes voiced segments and jitter/shimmer features based on the ablation study results. protocol described in Section 3.2. Table 4 presents a detailed comparison between our implementation and the original baseline results reported in [36]. While our implementation shows slightly higher DER values in most scenarios, the performance trends across different numbers of speakers and training configurations remain consistent with the original baseline. The differences in absolute performance can be attributed to variations in the simulated training data generation, despite following the same methodology. Both implementations demonstrate similar patterns: performance degradation as the number of speakers increases, and substantial improvements when incorporating multi-speaker training data (SC 1-4 spkr) and real conversational data adaptation (CH1 all). To provide a more comprehensive assessment of our baseline system's capabilities, we evaluated its performance across multiple metrics beyond DER as is shown in Table 5. In terms of speaker counting, the system achieves an accuracy of 73.20% in correctly identifying the number of speakers, with a mean Speaker Count Error (SCE) of 0.348 speakers. These results indicate a robust capability in estimating the number of participants in a conversation, particularly considering the challenging nature of multi-speaker scenarios. The speaker turn detection performance reveals some challenges in precisely identifying speaker transitions, with a precision of 62.41% and recall of 46.31%, resulting in an F1-score of 53.16%. For overlapped speech detection (OSD), the system demonstrates a precision of 64.90% and recall of 42.40%, yielding an F1-score of 51.30%. The high OSD accuracy of 87.95% should be interpreted in the context of the natural class imbalance in conversational speech, where non-overlapped segments are more prevalent. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Training Set** & **All** & **2 spk** & **3 spk** & **4 spk** & **5 spk** & **6 spk** \\ \hline \multirow{3}{*}{Baseline} & SC 2 spkr & 20.86 & 8.48 & 21.07 & 29.56 & 45.61 & 49.2 \\  & SC 1–4 spkr & 16.18 & 8.95 & 13.78 & 21.22 & 37.35 & 46.32 \\  & CH1 all & 16.07 & 10.03 & 14.35 & 19.3 & 30.67 & 46.94 \\ \hline \multirow{3}{*}{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 \\ \cline{1-1}  & CH1 all & 16.8 & 7.99 & 15.42 & 23.47 & 37.17 & 41.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between the original baseline [36] and our implementation on CH2 evaluation set. Results are shown for different training configurations: two-speaker simulated conversations (SC 2 spkr), one-to-four speaker simulated conversations (SC 1–4 spkr), and after adaptation using the complete CH1 set (CH1 all). DER (%) is reported for the complete evaluation set (All) and broken down by the number of speakers in each recording.