Summary of Integrating Audio, Visual, and Semantic Information For Enhanced Multimodal Speaker Diarization, by Luyao Cheng and Hui Wang and Siqi Zheng and Yafeng Chen and Rongjie Huang and Qinglin Zhang and Qian Chen and Xihao Li
Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization
by Luyao Cheng, Hui Wang, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method for speaker diarization integrates audio, visual, and semantic cues to improve the accuracy of segmenting an audio stream into homogenous partitions based on speaker identity. The existing unimodal acoustic-based approaches are limited by the ambiguities of audio signals, making multimodality essential for enhancing performance. The novel approach formulates multimodal modeling as a constrained optimization problem, leveraging pairwise constraints from visual connections among active speakers and semantic interactions within spoken content to cluster speakers. Experimental results on multiple datasets demonstrate that this method outperforms state-of-the-art speaker diarization methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of understanding who is talking when in an audio recording has been developed. This system looks at three types of information: what the people are saying, how they look, and what they’re doing. It uses this information to figure out who is speaking at any given time. This helps make it easier to understand conversations that happen naturally, without being scripted or controlled. The new method works better than previous methods that only looked at one type of information. |
Keywords
» Artificial intelligence » Optimization
