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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.25458 |
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| _version_ | 1866917246507417600 |
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| author | Shi, Jiacheng Du, Hongfei Hong, Y. Alicia Gao, Ye |
| author_facet | Shi, Jiacheng Du, Hongfei Hong, Y. Alicia Gao, Ye |
| contents | Large audio-language models (LALMs) exhibit strong zero-shot performance across speech tasks but struggle with speech emotion recognition (SER) due to weak paralinguistic modeling and limited cross-modal reasoning. We propose Compositional Chain-of-Thought Prompting for Emotion Reasoning (CCoT-Emo), a framework that introduces structured Emotion Graphs (EGs) to guide LALMs in emotion inference without fine-tuning. Each EG encodes seven acoustic features (e.g., pitch, speech rate, jitter, shimmer), textual sentiment, keywords, and cross-modal associations. Embedded into prompts, EGs provide interpretable and compositional representations that enhance LALM reasoning. Experiments across SER benchmarks show that CCoT-Emo outperforms prior SOTA and improves accuracy over zero-shot baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Plug-and-Play Emotion Graphs for Compositional Prompting in Zero-Shot Speech Emotion Recognition Shi, Jiacheng Du, Hongfei Hong, Y. Alicia Gao, Ye Artificial Intelligence Large audio-language models (LALMs) exhibit strong zero-shot performance across speech tasks but struggle with speech emotion recognition (SER) due to weak paralinguistic modeling and limited cross-modal reasoning. We propose Compositional Chain-of-Thought Prompting for Emotion Reasoning (CCoT-Emo), a framework that introduces structured Emotion Graphs (EGs) to guide LALMs in emotion inference without fine-tuning. Each EG encodes seven acoustic features (e.g., pitch, speech rate, jitter, shimmer), textual sentiment, keywords, and cross-modal associations. Embedded into prompts, EGs provide interpretable and compositional representations that enhance LALM reasoning. Experiments across SER benchmarks show that CCoT-Emo outperforms prior SOTA and improves accuracy over zero-shot baselines. |
| title | Plug-and-Play Emotion Graphs for Compositional Prompting in Zero-Shot Speech Emotion Recognition |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25458 |