# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 1 Introduction  
Speaker diarization refers to the task that automatically identifies and distinguishes between different speakers in an audio recording by analyzing unique voice characteristics. This technology enables numerous practical applications across diverse domains: in media production, it facilitates automatic subtitle generation with speaker identification [1]; in meeting transcription services, it creates navigable records with labeled speaker turns [2]; in forensic analysis, it helps identify speakers in courtroom recordings or surveillance audio [3]; in healthcare, it supports medical transcription with proper attribution of physician and patient speech [4, 5]; and in call center analytics, it enables evaluation of agent-customer interactions [6]. Speaker diarization also enhances automatic speech recognition (ASR) systems by providing speaker-specific acoustic models and enables long-form audio content indexing for efficient retrieval of specific speakers' contributions [7]. The evolution of diarization systems has seen multiple paradigms. Traditional approaches rely on clustering speaker embeddings using methods like Agglomerative Hierarchical Clustering [8] or the more sophisticated VBk [9], segmenting audio into speech andnon-speech chunks using Voice Activity Detection (VAD) [10] before extracting speaker representations, such as i-vectors [11], x-vectors [8], or ECAPA-TDNN embeddings [12]. These speaker representations, while effective, are not inherently trained to handle silence segments and overlapped speech [13], necessitating careful consideration of VAD in the diarization pipeline. Methods often face challenges with the trade-offs in embedding extraction window sizes [14]. The field has witnessed significant advancement with the introduction of end-to-end neural approaches [15; 16], which directly address the limitations of traditional methods. End-to-end approaches, particularly the Self-Attentive End-to-End Neural Diarization (SA-EEND) [17; 18], effectively handle overlapped speech and silence segments by treating diarization as a multi-label classification problem. The introduction of EEND-EDA (End-to-End Neural Diarization with Encoder-Decoder based Attractors) [19] further enhanced these systems' ability to handle varying numbers of speakers. We selected the EEND architecture for our investigation due to its established reliability, reproducibility, and adaptability to different feature representations. These models have formed a strong foundation for diarization research, with numerous subsequent advancements building upon their framework. Additionally, the availability of open-source implementations and synthetic data generation algorithms makes replication of these experiments more accessible to the research community, facilitating further innovation and validation of results. It is worth noting that while we use the term 'end-to-end' to describe these models, our application incorporates pre-trained features like ECAPA-TDNN embeddings, which strictly speaking introduces a separate processing stage. In our context, 'end-to-end' refers specifically to the direct processing from input features to speaker labels without intermediate clustering or segmentation steps. Recent research has explored various strategies to improve diarization performance. Some approaches leverage automatic speech recognition for word-level speaker turn detection [20; 21], while others propose hybrid systems combining end-to-end approaches with clustering algorithms [22; 23]. Additional advances have examined the impact of different speaker representations on diarization performance [24]. In the context of feature combination strategies, the Target-Speaker Voice Activity Detection (TS-VAD) system [25] marked a significant advancement in speaker diarization by demonstrating the effectiveness of incorporating speaker-specific information through feature concatenation.