“ISCSLP Tutorial 2”版本间的差异
来自cslt Wiki
（以“Prof. Chung-Hsien Arousal & Valence coordinator”为内容创建页面） |
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| 第1行： | 第1行： | ||
Prof. Chung-Hsien | Prof. Chung-Hsien | ||
| − | Arousal & Valence coordinator | + | * Arousal & Valence coordinator |
| + | * separate emotion process to sub emotions | ||
| + | |||
| + | * available databases: | ||
| + | * database collection: | ||
| + | :* acted : GEneva multimodeal emotion portrayals (GEMEP) | ||
| + | :* induced : eNTERFACE'05 EMOTION Database | ||
| + | :* spontaneous: SEMAINE, AFEW | ||
| + | : others: RML,VAM ,FAU AUBO,SAVEE,TUMAVIC,IEMOCAP,SEMAINE MHMC | ||
| + | |||
| + | * static vs dynamic modeling | ||
| + | |||
| + | STATIC: | ||
| + | :* low level descriptors (LLDs) and functionals | ||
| + | :* good for discriminate high and low-arousal emotions | ||
| + | :* temporal information is lost, no suitable for long utterances, can not detect change in emotion | ||
| + | |||
| + | DYNAMIC: | ||
| + | :* frame as the basis, LLDs are extracted and modeled by GMMs, HMMs, DTW | ||
| + | :* temporal information is obtained | ||
| + | :* difficult to model context well | ||
| + | :* a large number of local features need to be extracted | ||
| + | |||
| + | * Unit choice for dynamic modeling | ||
| + | :* technical unit: frame, time slice, equally-divided unit | ||
| + | :* meaningful unit: word, syllable, phrases | ||
| + | :* emotionally consistent unit: emotion profiles, emotograms | ||
| + | |||
| + | |||
| + | |||
| + | recognition models | ||
2014年9月13日 (六) 05:25的版本
Prof. Chung-Hsien
- Arousal & Valence coordinator
- separate emotion process to sub emotions
- available databases:
- database collection:
- acted : GEneva multimodeal emotion portrayals (GEMEP)
- induced : eNTERFACE'05 EMOTION Database
- spontaneous: SEMAINE, AFEW
- others: RML,VAM ,FAU AUBO,SAVEE,TUMAVIC,IEMOCAP,SEMAINE MHMC
- static vs dynamic modeling
STATIC:
- low level descriptors (LLDs) and functionals
- good for discriminate high and low-arousal emotions
- temporal information is lost, no suitable for long utterances, can not detect change in emotion
DYNAMIC:
- frame as the basis, LLDs are extracted and modeled by GMMs, HMMs, DTW
- temporal information is obtained
- difficult to model context well
- a large number of local features need to be extracted
- Unit choice for dynamic modeling
- technical unit: frame, time slice, equally-divided unit
- meaningful unit: word, syllable, phrases
- emotionally consistent unit: emotion profiles, emotograms
recognition models
