# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 5 Conclusions  
This research presented a thorough investigation into feature integration strategies for end-to-end neural speaker diarization, focusing on conversational telephone speech. Our most significant achievement is demonstrating that carefully optimized end-to-end neural diarization systems can achieve performance comparable to state-of-the-art clustering-based approaches like VBx, while offering crucial advantages in adaptability, unified architecture, and inherent handling of overlapped speech. This represents a major advancement in neural diarization technology, as end-to-end systems have traditionally lagged behind multi-stage clustering approaches in benchmark performance. The ECAPA-TDNN speaker embeddings, when combined with Mel-filterbank features, achieved the best overall performance with a 29% relative reduction in Diarization Error Rate (DER) through systematic adaptation strategy optimization. It is important to note that for this configuration, Oracle VAD was employed during both training and inference phases, addressing the known limitations of ECAPA embeddings in representing non-speech regions. For fair comparison, similar Oracle VAD conditions were applied to all other feature configurations in the two-speaker scenario, ensuring consistent evaluation conditions across all experiments. Despite these controlled conditions, the ECAPA-TDNN with MFB configuration demonstrated superior capability in detecting speech segments with the lowest miss rate among all tested configurations. GeMAPS paralinguistic features showed distinct strengths in speaker attribution precision and boundary detection, achieving the lowest false alarm and speaker confusion rates. However, their higher miss rate limited overall DER performance. The study also identified a reduced optimal set of 52 GeMAPS parameters that maintained performance while reducing computational complexity. The research revealed that adaptation strategies significantly impact performance, with restricted adaptation to similar speaker counts providing consistent benefits across all \begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{3}{c}{**DER (\%)**} \\ \cline{3-5}
**System** & **Collar** & \
\begin{tabular}{c} **Subsampling = 10,** \\ **Median = 11** \\ \end{tabular} &
\begin{tabular}{c} **Subsampling = 5,** \\ **Median = 5** \\ \end{tabular} \\ \hline \multirow{2}{*}{MFB} & ✓ & 15.72 & 16.76 \\  & ✗ & 26.06 & 23.95 \\ \hline \multirow{2}{*}{ECAPA-TDNN \(\oplus\) MFB} & ✓ & 14.89 & 15.59 \\  & ✗ & 25.78 & 22.69 \\ \hline \multirow{2}{*}{GeMAPS \(\oplus\) MFB} & ✓ & 18.87 & 23.25 \\  & ✗ & 29.19 & 26.94 \\ \hline \multirow{2}{*}{VBx [9]} & ✓ & 14.21 & \\  & ✗ & 21.77 & \\ \hline \multirow{2}{*}{AHC [9]} & ✓ & 17.64 & \\  & ✗ & 25.61 & \\ \hline \multirow{2}{*}{Pyannote [44]} & ✓ & - & \\  & ✗ & 29.30 & \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Performance comparison between our optimized systems and state-of-the-art approaches on the CH2 evaluation set. Results are shown for both collared (250 ms) and collar-free evaluation, applying different subsampling factors during inference for our proposed systems. feature configurations. This finding challenges conventional wisdom [36] about adaptation to the complete range of potential speakers. Enhanced temporal resolution through reduced subsampling proved highly beneficial, particularly for speaker-discriminative features. For ECAPA-TDNN with MFB features, this approach yielded a 12% relative improvement in collar-free evaluation, highlighting the importance of precise temporal analysis in diarization. The optimized ECAPA-TDNN with MFB features configuration achieved competitive performance against state-of-the-art systems, including specialized clustering-based methods, while maintaining the adaptability advantages of end-to-end neural approaches.