Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech

This paper addresses the challenge of optimizing end-to-end neural diarization systems for conversational telephone speech, focusing on diverse acoustic features beyond traditional Mel-filterbanks. We present a methodological framework for integrating and analyzing different feature types as input to the well-known End-to-End Neural Diarization with Encoder Decoder Attractors (EEND-EDA) model, focusing on Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) embeddings and Geneva Minimalistic Acoustic Parameter Sets (GeMAPS). Our approach combines systematic feature analysis with adaptation strategies, including speaker-count restriction and regularization techniques. Moreover, through comprehensive ablation studies of GeMAPS features, we identify optimal acoustic parameters and temporal contexts for diarization tasks, achieving a reduced feature set that maintains performance while decreasing computational complexity. Experiments on the CallHome corpus demonstrate that our optimized ECAPA-TDNN with Mel-filterbank combination reduces Diarization Error Rate by 29% relative to baseline systems. Our evaluation framework extends beyond traditional metrics, revealing that different feature combinations exhibit distinct strengths in specific diarization aspects.