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d458c13 d68f624 d458c13 bc5f9ee d458c13 bc5f9ee d458c13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | # HumDial-EIBench
> A human-recorded multi-turn emotional intelligence benchmark for audio language models.
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[](https://arxiv.org/pdf/2604.11594) [](https://huggingface.co/datasets/ASLP-lab/HumDial-EIBench) [](https://github.com/ASLP-lab/HumDial-EIBench)
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<div align="center">
<img src="humdial-bench.png" alt="HumDial-EIBench pipeline and task overview" width="90%">
<p><em>Figure: Three-stage data pipeline and the four evaluation tasks in HumDial-EIBench.</em></p>
</div>
HumDial-EIBench is designed to evaluate whether audio language models (ALMs) truly understand emotion in speech, rather than relying on text transcription shortcuts.
The benchmark is built from authentic human-recorded dialogues from the ICASSP 2026 HumDial Challenge and includes both Chinese and English subsets.
---
## Why HumDial-EIBench
Existing ALM benchmarks often suffer from one or more of these issues:
- synthetic (TTS-only) speech instead of authentic human recordings
- single-turn settings that miss emotional evolution over context
- subjective open-ended scoring that confounds reasoning and generation quality
HumDial-EIBench addresses these gaps by combining:
- **real human multi-turn audio**
- **objective adversarial MCQ tasks for reasoning-heavy evaluation**
- **a dedicated acoustic-semantic conflict task**
- **separate diagnosis of textual empathy vs acoustic empathy**
---
## Benchmark at a Glance
- **Total samples:** 1,077
- **Languages:** Chinese + English
- **Core goal:** diagnose emotional intelligence in ALMs across memory, reasoning, generation, and cross-modal robustness
| Task | Type | CN / EN | Turns | Main Metric |
| :--- | :---: | :---: | :---: | :---: |
| **Task 1: Emotional Trajectory Detection** | MCQ | 150 / 150 | 3-5 | Accuracy |
| **Task 2: Implicit Causal Reasoning** | MCQ | 134 / 149 | 3-5 | Accuracy |
| **Task 3: Empathetic Response Generation** | Open Generation | 144 / 150 | 3-5 | LLM + Human |
| **Task 4: Acoustic-Semantic Conflict** | MCQ | 100 / 100 | 1 | Accuracy |
| **Total** | | **528 / 549** | | |
---
## Four Tasks
### Task 1: Emotional Trajectory Detection
Track emotion changes across dialogue turns (for example, `E_t1 -> E_t2 -> E_t3`), instead of classifying isolated utterances.
### Task 2: Implicit Causal Reasoning
Infer the latent emotional trigger from scattered context clues. The MCQ format helps reduce evaluator subjectivity.
### Task 3: Empathetic Response Generation
Evaluate generated responses in three dimensions:
- **D1: Textual Empathy & Insight** (LLM-judge, 1-5)
- **D2: Vocal Empathy & Congruence** (human rating, 1-5)
- **D3: Audio Quality & Naturalness** (human rating, 1-5)
### Task 4: Acoustic-Semantic Conflict
Test robustness when text sentiment contradicts vocal affect (for example, sarcasm-like cases), exposing text-dominance bias.
---
## Key Findings
- Most ALMs still struggle with **multi-turn emotional tracking** and **implicit causal reasoning**.
- Strong **decoupling exists between textual empathy and acoustic empathy**.
- All tested models show a notable **text-dominance bias** under acoustic-semantic conflict.
---
---
## Data and Code Access
- The dataset is publicly available on HuggingFace: [HumDial-EIBench](https://huggingface.co/datasets/ASLP-lab/HumDial-EIBench)
---
## Evaluation Usage
### Task 3 (Empathetic Generation) Scoring
`eval/eval_task3.py` scores model outputs for D1/D2/D3 and writes per-sample + summary results.
#### Input format (`jsonl`)
```jsonc
{
"dialogue_id": "sample_001",
"turns": [
{
"input_emotion": "sad",
"input_text": "I've been feeling really overwhelmed lately...",
"response_text": "It sounds like you're carrying a lot right now.",
"response_audio": "outputs/sample_001_turn1.wav"
}
]
}
```
#### Run
```bash
python eval/eval_task3.py \
--model Qwen3-Omni-30B-A3B-Instruct \
--input_file results/task3_outputs.jsonl \
--output_file results/task3_scores.jsonl
```
The script automatically identifies the target evaluation turn (second non-neutral turn) and builds context from prior turns.
> Environment note: this script requires a GPU runtime and `vLLM`. Set the local judge checkpoint path in `eval/eval_task3.py` before running.
---
---
## Citation
If you find this work useful, please cite:
```bibtex
@misc{wang2026humdialeibenchhumanrecordedmultiturnemotional,
title={HumDial-EIBench: A Human-Recorded Multi-Turn Emotional Intelligence Benchmark for Audio Language Models},
author={Shuiyuan Wang and Zhixian Zhao and Hongfei Xue and Chengyou Wang and Shuai Wang and Hui Bu and Xin Xu and Lei Xie},
year={2026},
eprint={2604.11594},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2604.11594},
}
```
## License
The code in this repository is released under the **Apache 2.0 License**.
## Contact
- **Issues**: Please open a GitHub Issue for bug reports or suggestions.
- **Email**: wangshuiyuan@mail.nwpu.edu.cn, lxie@nwpu.edu.cn
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