# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.3 Domain-Focused Adaptation Analysis  
Domain adaptation strategies plays a crucial role in optimizing diarization performance. Figure 6 illustrates the comparative impact of adaptation approaches across different feature configurations, specifically contrasting adaptation using the complete CH1 dataset (containing conversations with 2-7 speakers) versus a restricted subset containing only conversations with 2-4 speakers, which more closely resembles the original training conditions. For the MFB baseline system Figure 6a, restricting the adaptation set to 2-4 speakers shows consistent improvements across all metrics. The DER decreases from 16.80% to 15.64%, while speaker counting becomes more reliable with SCE improving from 0.348 to 0.320 and accuracy increasing from 73.60% to 74.40%. Both speaker turn detection and overlap speech detection show slight improvements (F1-scores increasing from 53.2% to 54.0% and 51.3% to 52.6% respectively). The ECAPA-TDNN \(\oplus\) MFB configuration Figure 6b demonstrates the most substantial benefits from restricted adaptation. DER significantly improves from 20.95% to 17.09%, a relative improvement of 18.4%. Speaker counting metrics show notable enhancement, with SCE decreasing from 0.444 to 0.376 and accuracy increasing from 64.0% to 68.4%. Turn detection F1-score improves from 45.32% to 48.33%, and OSD performance increases from 44.0% to 46.1% F1-score. For GeMAPS \(\oplus\) MFB Figure 6c, the improvements are more modest but still consistent. DER shows a slight improvement from 23.21% to 22.50%, while speaker counting metrics improve marginally (SCE from 0.428 to 0.415, accuracy from 66.4% to 67.3%). However, turn detection performance shows a small degradation (F1-score from 48.21% to 47.09%), though this is compensated by improved OSD performance (F1-score from 44.0% to 45.27%). These results demonstrate that feature selection significantly influences the effectiveness of domain adaptation in EEND systems. The ECAPA-TDNN embeddings, when concatenated with MFB features, show heightened sensitivity to the distribution of speakers in the adaptation data. This suggests that pre-trained speaker embeddings retain their  
Figure 6: Performance comparison across feature configurations when adapting to different CH1 subsets. Radar plots show multiple evaluation metrics for: (**a**) MFB baseline, (**b**) ECAPA-TDNN \(\oplus\) MFB, and (**c**) GeMAPS \(\oplus\) MFB. Each plot compares adaptation using the complete CH1 all set (2–7 speakers) versus using only conversations with 2-4 speakers. Beside each metric, arrows indicate whether lower (\(\downarrow\)) or higher (\(\uparrow\)) values are better. Values in parentheses show the metric scores for (CH1 all, CH1-2–4 spk) respectively. Numerical ranges shown in brackets [min–max] for each axis are individually adjusted per metric to highlight relative differences between adaptation strategies. Metrics displayed include DER, SCE, Speaker Count Accuracy, Turn Detection F1, and OSD F1. speaker discriminative properties when used as input features for the EEND framework, making them more responsive to targeted adaptation strategies. The differential response across feature types indicates a fundamental property of acoustic representations in diarization: features that excel at speaker discrimination (such as ECAPA-TDNN embeddings) benefit more substantially from adaptation data that closely matches the target domain's speaker distribution. This is likely because these features emphasize speaker-specific characteristics that vary more significantly with the number of participants in a conversation. From a practical implementation perspective, these findings suggest that adaptation strategies should be customized based on the selected feature representation. While MFB features show consistent but modest improvements with restricted adaptation, systems utilizing speaker embedding features should prioritize adaptation data that closely aligns with the expected deployment conditions, particularly in terms of speaker count.