End-to-end neural speaker diarization systems are able to address the speaker
diarization task while effectively handling speech overlap. This work explores
the incorporation of speaker information embeddings into the end-to-end systems
to enhance the speaker discriminative capabilities, while maintaining their
overlap handling strengths. To achieve this, we propose several methods for
incorporating these embeddings along the acoustic features. Furthermore, we
delve into an analysis of the correct handling of silence frames, the window
length for extracting speaker embeddings and the transformer encoder size. The
effectiveness of our proposed approach is thoroughly evaluated on the CallHome
dataset for the two-speaker diarization task, with results that demonstrate a
significant reduction in diarization error rates achieving a relative
improvement of a 10.78% compared to the baseline end-to-end model.

本研究通过将发言者信息嵌入到端到端系统中，提高了发言者辨识能力，并保持了处理语音重叠的优势，通过多种方法将这些嵌入与声学特征相结合。同时，对处理静默帧、提取发言者嵌入的窗口长度和变压器编码器尺寸进行了深入分析。在 CallHome 数据集上对双发言者分析任务进行了全面评估，结果表明相对于基准端到端模型，发现有了显著的降低对话错误率，相对提高了 10.78%。