SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark
Understanding human emotions in spoken conversations is a key challenge in affective computing, with applications in empathetic AI, human computer interaction, and mental health monitoring. However, existing datasets vary in scale, emotion distribution, modality alignment, and data partitioning strategies, which can influence reliable cross-dataset generalization and minority-emotion modeling. We introduce SpEmoC a Speaking segment Emotion for Conversations comprising 306,544 raw clips from 3,100 English language movies and TV series. From these, 30,000 high quality, class balanced clips are c
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 59%pyannote/speaker-diarization-3.1 →
“Fuzzy title match (0.73): “SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchm” ≈ “pyannote/speaker-diarization-3.1””
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchm” ≈ “jeinlee1991/chinese-llm-benchmark””
- LinkedLinked via arxiv author · 85%Sania Bano →
“SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark”
- LinkedLinked via arxiv author · 85%Shahzad Ahmad →
“SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark”
- LinkedLinked via arxiv author · 85%Santosh Kumar Vipparthi →
“SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark”
- LinkedLinked via arxiv author · 85%Sukalpa Chanda →
“SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark”
- LinkedLinked via arxiv author · 85%Subrahmanyam Murala →
“SpEmoC: A Balanced Speaker-Segment Multimodal Emotion Benchmark”