# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Two-Speaker Scenario Performance
#### 4.1.1 GeMAPS Analysis  
Our analysis of GeMAPS features consists of two complementary studies: a feature importance assessment through ablation and an investigation of temporal context impact. For these experiments, we used a context size of 2 frames for the initial feature importance evaluation, resulting in a 310-dimensional input vector (5 \(\times\) 62 features), which is comparable to the baseline MFB configuration (345 features) in terms of dimensionality. Feature Group Importance (Inference Stage)  
Figure 4 shows the DER degradation (\(\Delta\)DER%) when different feature groups are perturbed during inference. In this context, \"perturbed during inference\" refers to averaging each parameter in the corresponding feature group across all frames in the utterance, effectively neutralizing their discriminative information while maintaining the overall structure. The formant-related features (14 parameters) show the highest impact, with a 27.28% DER increase when masked, followed by fundamental frequency features (F0, 10 parameters) with an 18.85% degradation. Frequency ratios and loudness parameters also demonstrate substantial importance, causing 15.42% and 15.24% DER increases respectively. In contrast, jitter/shimmer and voiced segment features show minimal impact (0.2% DER change), suggesting their limited contribution to speaker discrimination in our diarization framework. For more details about the fundamentals of the GeMAPS features, see [30]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \multirow{2}{*}{**Feature Type**} & \multirow{2}{*}{**Conditions**} & \multirow{2}{*}{**Adaptation**} & \multicolumn{5}{c}{**DER Components (\%)**} \\ \cline{4-7}  & & & **Miss** & **FA** & **Spk. Error** & **Total** \\ \hline \multirow{2}{*}{MFB (baseline)} & \multirow{2}{*}{No VAD} & ✗ & 2.50 & 6.60 & 0.99 & 10.09 \\  & & ✓ & 3.40 & 3.87 & 0.80 & 8.07 \\ \hline \multirow{2}{*}{ECAPA-TDNN \(\oplus\) MFB} & \multirow{2}{*}{Oracle VAD} & ✗ & 3.55 & 3.10 & 3.21 & 9.86 \\  & & ✓ & 3.0 & 3.05 & 1.15 & 7.20 \\ \hline \multirow{2}{*}{GeMAPS \(\oplus\) MFB} & \multirow{2}{*}{No VAD} & ✗ & 3.50 & 5.70 & 1.55 & 10.75 \\  & & ✓ & 5.74 & 2.40 & 0.90 & 9.04 \\ \hline \end{tabular}
\end{table} 
Table 2: Diarization Error Rate (DER) components for best feature configurations. For ECAPA-TDNN concatenated with MFB features, oracle VAD was employed during both training and inference phases. Models are adapted to CH1-2 spk and evaluated on CH2-2 spk. Feature Set Optimization (Training Stage)  
Based on the previous results, three EEND-EDA models were trained using SC 2 spk with different GeMAPS configurations as proposed in Table 3. While intuition might suggest that fundamental frequency and formant features (24 parameters) would be sufficient for speaker discrimination, this reduced configuration significantly degrades performance, increasing DER to 45.33%. This substantial degradation can be attributed to the limited spectral information available in the 8 kHz telephone data used in the two-speaker scenario. At this sampling rate, the frequency range is constrained to 4 kHz, which severely restricts the discriminative power of formants, particularly for higher formants that often contain speaker-specific characteristics. Additionally, in telephone conversations, the standard telephone bandwidth filtering (approximately 300-4000 Hz) removes critical acoustic information, including higher formants and spectral details that would normally help distinguish between speakers. This bandwidth limitation, combined with channel effects and compression artifacts, significantly compromises fundamental frequency estimation accuracy, further reducing the effectiveness of these features when used in isolation. However, our analysis enabled the identification of a reduced feature set that maintains performance while eliminating less contributive features.