# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.4 Enhanced Adaptation Techniques  
Having demonstrated the benefits of restricted adaptation (CH1 2-4), we explore additional techniques to further improve the adaptation process. These enhancements aim to address specific challenges in the adaptation phase: overfitting (through increased dropout), class imbalance (through weighted sampling), and annotation precision (through label smoothing). Dropout Regularization  
The impact of increased dropout rates during adaptation shows distinct patterns across different feature configurations, as illustrated in Figure 7. For the baseline MFB features, as shown in Table 8, moderate dropout (0.3) maintains performance stability while providing minor improvements in speaker turn detection, with the F1-score increasing from 53.16% to 54.13% when using the complete adaptation set. However, higher dropout rates (0.5) lead to significant degradation across all metrics, particularly affecting overlap speech detection where the F1-score decreases dramatically from 51.3% to 30.4%. The ECAPA-TDNN \(\oplus\) MFB configuration, detailed in Table 9, demonstrates the most notable benefits from increased regularization, especially when combined with restricted  
Figure 7: Impact of dropout rate on DER performance across different feature configurations. Results compare adaptation using complete CH1 all (2–7 speakers) versus restricted set (2–4 speakers). Each subplot shows a different feature configuration: (left) MFB baseline features, (middle) ECAPA-TDNN \(\oplus\) MFB features, and (right) GeMAPS \(\oplus\) MFB features. adaptation. With a dropout rate of 0.3 and CH1 2-4 spk adaptation, the system achieves its best performance with a DER of 15.8%, representing a 24.6% relative improvement over the baseline configuration (20.95%). This enhancement extends beyond DER, with improved speaker counting accuracy (68.8%) and more robust turn detection (F1-score of 48.89%). The results suggest that this feature combination particularly benefits from the regularizing effect of dropout in preventing overfitting to the small adaptation set. For GeMAPS \(\oplus\) MFB features, Table 10 reveals that the impact of dropout varies significantly between adaptation strategies. When using the complete adaptation set, increased dropout leads to substantial performance degradation, with DER rising from 23.21% to 31.67% at 0.3 dropout and further deteriorating to 32.35% at 0.5 dropout. However, with restricted adaptation, moderate dropout (0.3) yields notable improvements, reducing DER to 19.74% and enhancing speaker counting accuracy from 67.3% to 73.2%. This dichotomy suggests that the effectiveness of dropout for GeMAPS features is highly dependent on the adaptation data distribution, with better results achieved when maintaining speaker count consistency between training and adaptation phases. Across all feature configurations, the experiments reveal a consistent pattern: moderate dropout (0.3) combined with restricted adaptation provides the most stable and effective regularization strategy. This combination helps mitigate overfitting while maintaining the model's ability to capture speaker-specific characteristics, particularly beneficial for speaker counting and turn detection tasks. However, the results also emphasize the importance of careful dropout rate selection, as excessive regularization (0.5) consistently leads to performance degradation, particularly affecting the system's ability to detect overlapped speech segments.