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About this paper
Qirui Zhan, Shuiyuan Wang, Jingbin Hu, Haoyu Zhang, Xiaming Ren, Jinrui Liang, Chaoren Yu, Bengu Wu and 5 more
SpeechAnnotator presents a context-aware multi-agent framework for multidimensional speech annotation, along with a unified benchmark (SA-Bench) and comprehensive evaluation protocol (SA-Eval). This framework, built on open-source components, achieves competitive performance in both timeline recovery and segment-level attribute annotation, reaching an 81.27% attribute macro average on human-annotated data.
Key insights
- Integrating diverse contextual evidence (speaker history, neighboring segments, global recording summary) and prior acoustic/affective cues significantly improves annotation quality and consistency, especially for semantic and speaker-dependent attributes.
- A bounded review loop with field-specific revision requests is crucial for stabilizing subjective attribute predictions and resolving inconsistencies, leading to noticeable gains in annotation stability.
- An open-source, multi-agent architecture can effectively coordinate complex multidimensional annotation tasks, reducing reliance on proprietary services while maintaining competitive performance.
Summary by alphaXiv.
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