Multimodal Fusion for Speaker Diarization
Multimodal Fusion for Speaker Diarization is a Computer vision skill for AI agents, published by benchflow-ai in the skillsbench repository on GitHub. Skillspace rates Multimodal Fusion for Speaker Diarization D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Combine visual features (face detection, lip movement analysis) with audio features to improve speaker diarization accuracy in video files. Use OpenCV for face detection and lip movement tracking, then fuse visual cues with audio-based speaker embeddings. Essential when processing video files with multiple visible speakers or when audio-only diarization needs visual validation.
- Grade
- D
- Quality
- 66 / 100
- Safety
- Passed
- GitHub stars
- 1,769
Unproven: rated by static analysis only, not yet compared head-to-head.
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Our review
- Architecture
- 97 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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