# Improving Speaker Diarization using Semantic Information: Joint Pairwise Constraints Propagation

**Authors:** Luyao Cheng, Siqi Zheng, Qinglin Zhang, Hui Wang, Yafeng Chen, Qian Chen, Shiliang Zhang  
**arXiv:** [2309.10456](https://arxiv.org/abs/2309.10456)  
**Date:** 2023-09-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.10456v1/

## Key points

- The paper focuses on integrating semantic information in speaker diarization systems to enhance performance.
- Traditional methods rely only on acoustic signals and overlook the potential of semantic cues present in speech content.
- The authors propose a novel approach that uses spoken language understanding modules to extract speaker-related semantic information and construct pairwise constraints.
- These constraints are integrated into the speaker diarization pipeline, leading to improved system performance.
- Experimental results show that the Joint Pairwise Constraints Propagation (JPCP) method demonstrates a 19% increase in TextDER and some improvement in SpkDiff metrics compared to baseline approaches.
- Incorporating semantic information alongside acoustic signals is crucial for improving speaker diarization tasks.
- The quality of constraints plays a significant role in achieving performance improvements, with sensitivity shown by both JPCP and E2CPM methods.
- Effectively leveraging semantic cues can advance clustering-based speaker diarization techniques.

## Summary

The paper "Improving Speaker Diarization using Semantic Information: Joint Pairwise Constraints Propagation" by Luyao Cheng et al. delves into the integration of semantic information in speaker diarization systems to enhance performance. Traditional methods rely solely on acoustic signals and overlook the potential of semantic cues present in speech content. The authors propose a novel approach that utilizes spoken language understanding modules to extract speaker-related semantic information and construct pairwise constraints. These constraints are then integrated into the speaker diarization pipeline, resulting in improved system performance. Experimental results on a public dataset demonstrate the superiority of this approach over acoustic-only speaker diarization systems. Specifically, the Joint Pairwise Constraints Propagation (JPCP) method shows a 19% increase in TextDER and some improvement in SpkDiff metrics compared to baseline approaches. This study highlights the importance of incorporating semantic information alongside acoustic signals in speaker diarization tasks. The authors emphasize that the quality of constraints plays a crucial role in achieving performance improvements, with both JPCP and E2CPM methods showing sensitivity to constraint quality. In conclusion, this research contributes to advancing speaker diarization techniques by effectively leveraging semantic cues for clustering-based systems. can greatly benefit from incorporating  through , which can be extracted using . It is evident that utilizing both acoustic signals and semantic cues leads to improved performance, highlighting their complementary nature in speaker diarization tasks.