# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis  
### Two-Speaker Scenario Performance  
Figure 3 presents the diarization performance for different feature configurations in two-speaker conversations on the CH2 CallHome (CH2-2 spk). The evaluation encompasses our proposed feature types (ECAPA-TDNN, GeMAPS, and eGeMAPS) compared against the baseline MFB features, both as standalone inputs and in concatenated configurations.  
The baseline MFB features achieve a competitive DER of 10.09% without adaptation, which improves to 8.07% when domain adaptation is applied, without requiring VAD processing. When evaluating individual non-adapted features without MFB concatenation, GeMAPS features demonstrate the strongest standalone performance with a DER of 15.0%, compared to 23.5% for ECAPA-TDNN with Oracle VAD and 29.6% for eGeMAPS features. These results indicate that individual features, despite their specialized design, do not outperform the traditional MFB approach on their own.  
Feature concatenation with MFB yields substantial improvements across all configurations. Without adaptation, ECAPA-TDNN \(\oplus\) MFB yields a DER of 9.9%, while GeMAPS \(\oplus\) MFB achieves 13.4% and eGeMAPS \(\oplus\) MFB shows 24.7%. With adaptation, the ECAPA-TDNN \(\oplus\) MFB combination achieves the best performance with a DER of 7.2% under Oracle VAD conditions, representing a 10.8% relative improvement over the baseline. Similarly, adapted GeMAPS \(\oplus\) MFB achieves a DER of 9.9%, while adapted eGeMAPS \(\oplus\) MFB reduces the error to 9.1%.  
The impact of Oracle VAD varies significantly across feature configurations. For ECAPA-TDNN \(\oplus\) MFB, the absence of Oracle VAD leads to performance degradation, with DER increasing from 7.2% to 12.6%, which aligns with the known limitations of  
Figure 3: Results obtained on CH2-2 spk dataset using different features. Shaded regions indicate experiments where the corresponding features were concatenated with MFB features. For each feature type. the bars show the DER with and without Oracle VAD, as well as with and without domain adaptation (striped bars indicate experiments with domain adaptation, solid bars without domain adaptation).  
ECAPA embeddings in representing non-speech segments. However, GeMAPS \(\oplus\) MFB shows an interesting pattern: when combined with domain adaptation, it achieves better performance without Oracle VAD (9.0% DER) than with it (13.4% DER). This suggests that GeMAPS features might inherently capture speech activity information more effectively, making the system more robust when explicit VAD is not available.  
Table 2 provides a detailed breakdown of the DER components for the best feature configurations. These optimal configurations will be utilized in subsequent multi-speaker experiments. The adapted ECAPA-TDNN \(\oplus\) MFB system achieves the lowest overall DER (7.20%), with balanced errors across miss detection (3.0%), false alarm (3.05%), and speaker error (1.15%) components. Notably, the adapted GeMAPS \(\oplus\) MFB system demonstrates the lowest false alarm rate (2.40%) and speaker error (0.90%), suggesting that these features excel at precisely identifying speech boundaries and distinguishing between speakers, particularly where background noise is present, despite having a higher miss rate (5.74%) than other configurations.