# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Comparison with State-of-the-Art Systems  
To provide a comprehensive evaluation of our proposed approaches, we compared our best-performing systems against several state-of-the-art diarization frameworks. This comparison includes both traditional clustering-based methods (VBx, AHC) and newer neural approaches (Pyannote). Additionally, we investigated the impact of temporal resolution by evaluating our systems with different subsampling rates, which offers important insights into the trade-off between computational efficiency and diarization accuracy. Table 20 presents a comparison of our systems against state-of-the-art approaches. Several key observations emerge from these results:  
First, the impact of temporal resolution (controlled by subsampling rate and median filter length) varies significantly across feature configurations. During inference, a median filter is applied to smooth the output predictions. The standard implementation uses an 11-frame median filter when employing a subsampling factor of 10. The length of this filter must be adjusted proportionally to the subsampling rate; therefore, when using a subsampling factor of 5, the median filter length is reduced to 5 frames. For ECAPA-TDNN \(\oplus\) MFB, finer temporal precision (subsampling = 5, median = 5) substantially improves collar-free performance, reducing DER from 25.78% to 22.69%, a remarkable 12.0% relative improvement. This suggests that speaker-discriminative embeddings particularly benefit from finer temporal granularity for precise boundary detection. Similarly, GeMAPS \(\oplus\) MFB shows an 7.7% relative improvement in collar-free evaluation when using finer temporal precision. In contrast, for MFB features, while collar-free performance improves with enhanced temporal resolution (26.00% to 24.21%), collared performance actually degrades (15.64% to 17.03%). This indicates that the benefit of enhanced resolution for MFB features manifests primarily at speaker boundaries, but may introduce additional errors in broader segment attribution. When comparing against established diarization approaches, our ECAPA-TDNN \(\oplus\) MFB configuration with enhanced temporal resolution (22.69% DER without collar) outperforms both AHC (25.61%) and Pyannote (29.30%), while approaching the performance of VBx (21.77%). Notably, the VBx system incorporates specialized x-vector embeddings and Bayesian HMM refinement, making it a highly optimized clustering-based approach. The competitive performance of our end-to-end neural system demonstrates the effectiveness of our adaptation strategy and feature integration approach. For collared evaluation, our ECAPA-TDNN \(\oplus\) MFB with standard temporal resolution achieves 14.89% DER, which is competitive with VBx (14.21%) and substantially better than AHC (17.64%). This suggests that our approach effectively captures the broader speaker attribution patterns while maintaining reasonable boundary precision. The consistent performance advantage of our ECAPA-TDNN \(\oplus\) MFB configuration, particularly with enhanced temporal resolution, highlights the value of combining acoustic features with specialized speaker embeddings. Furthermore, the dramatic impact of temporal resolution adjustments--which come with minimal computational overhead--demonstrates that significant performance gains can be achieved through careful parameter tuning without increasing model complexity. These findings establish our adapted ECAPA-TDNN \(\oplus\) MFB with enhanced temporal resolution as a highly competitive approach for speaker diarization, capable of rivaling and, in certain metrics, surpassing specialized clustering-based methods while maintaining the adaptability and end-to-end nature of neural diarization frameworks.