# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 1 Introduction
First introduced in the CHiME-6 Challenge [26], where it achieved state-of-the-art performance and led to winning the competition, this approach enhanced speaker diarization by combining traditional Mel-filterbank features with i-vector speaker embeddings. The remarkable success of this feature integration strategy, which significantly outperformed other participating systems, has inspired various extensions and improvements [27; 28], suggesting the potential benefits of exploring similar approaches with other types of acoustic features. This approach of combining different feature sets has proven successful in related speech processing tasks, particularly in paralinguistic applications. Research demonstrated the effectiveness of integrating Mel-filterbank features with The Geneva Minimalistic Acoustic Parameter Sets (GeMAPS and eGeMAPS) [29; 30] for emotion recognition tasks, achieving improved performance through their complementary characteristics. GeMAPS represent a particularly promising feature set for such integration. These standardized sets of Low-Level Descriptors (LLDs) (LLDs capture instantaneous acoustic properties of the signal (e.g., F0, energy, formants) at short time frames, whereas High-Level Descriptors aggregate these primary features through statistical functionals (mean, variance, percentiles, etc.) to represent broader temporal patterns and paralinguistic characteristics.) demonstrated remarkable effectiveness in emotion recognition and speaker state analysis [31; 32], capturing a comprehensive range of voice characteristics including frequency-related, energy-related, spectral, and voice quality parameters. Their proven capability to discriminate speaker-specific traits suggests potential benefits for speaker diarization applications [13]. Building upon our previous work [33], this study presents a more comprehensive analysis of current state-of-the-art speaker diarization model performance, with a specific focus on conversational telephonic speech. The contributions of this work are summarized as follows:  
1. A systematic investigation of GeMAPS feature sets in end-to-end diarization models, including comprehensive ablation studies to identify optimal acoustic parameters and their temporal contexts. This analysis provides the first detailed guidelines for effectively integrating these paralinguistic features into neural diarization frameworks. 2. A comprehensive investigation of EEND-EDA's adaptability to different acoustic representations, demonstrating that careful hyperparameter optimization enables effective integration of both ECAPA-TDNN embeddings and GeMAPS features alongside traditional Mel-filterbank features, achieving significant performance improvements over conventional single-feature approaches. 3. Proposed adaptation strategies focusing on speaker-count restriction and enhanced regularization techniques, revealing that targeted optimization can yield substantial improvements in diarization performance across different feature types. Our approach achieves a 29% relative reduction in Diarization Error Rate through systematic hyperparameter tuning. 4. A detailed analysis of temporal resolution's impact on diarization performance, challenging conventional subsampling approaches and demonstrating the importance of fine-grained temporal analysis for different feature combinations. 5. A multi-metric evaluation framework that extends beyond traditional Diarization Error Rate (DER), providing deeper insights into system behavior across speaker counting, turn detection, and overlap speech handling capabilities. Evaluating on the CallHome corpus [34], a widely-used benchmark for conversational telephone speech, our systematic analysis of different feature combinations reveals their distinct strengths: ECAPA-TDNN with Mel-filterbank features excel in overlap speech detection and speaker transition identification, particularly important in natural dialogue, while GeMAPS-based configurations show superior performance in speaker detection precision despite channel variability.