基於多重注意力機制的輔助損失函數用於端到端語者標記

This study introduces a novel auxiliary function for use in the Self-Attention End-to-End Speaker Diarization (SA-EEND) model, aiming to achieve accurate speaker label prediction within overlapping speech regions. Previous research has lacked effective methods for leveraging speaker information within the model to enhance auxiliary model training and has not taken into account variations in the distribution of different speech activity patterns. This study proposes a novel auxiliary function to facilitate speaker label prediction within overlapping speech regions. By considering both the overall speech activity patterns and the task-specific speech activity patterns for different speakers, we adjust the weight matrices of the multi-head self-attention mechanism in the Transformer layers. We also select loss functions that can improve the learning performance for labels with fewer occurrences, resulting in better speaker discrimination. Experimental evaluations were conducted on Mini LibriSpeech. Although the results exhibited some limitations, there were still notable advancements made.