| # HumDial-EIBench |
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| > A human-recorded multi-turn emotional intelligence benchmark for audio language models. |
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| <div align="center"> |
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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> |
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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> |
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| HumDial-EIBench is designed to evaluate whether audio language models (ALMs) truly understand emotion in speech, rather than relying on text transcription shortcuts. |
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| The benchmark is built from authentic human-recorded dialogues from the ICASSP 2026 HumDial Challenge and includes both Chinese and English subsets. |
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| --- |
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| ## Why HumDial-EIBench |
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| Existing ALM benchmarks often suffer from one or more of these issues: |
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| - 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 |
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| HumDial-EIBench addresses these gaps by combining: |
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| - **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** |
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| --- |
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| ## Benchmark at a Glance |
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| - **Total samples:** 1,077 |
| - **Languages:** Chinese + English |
| - **Core goal:** diagnose emotional intelligence in ALMs across memory, reasoning, generation, and cross-modal robustness |
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| | 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** | | | |
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| --- |
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| ## Four Tasks |
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| ### Task 1: Emotional Trajectory Detection |
| Track emotion changes across dialogue turns (for example, `E_t1 -> E_t2 -> E_t3`), instead of classifying isolated utterances. |
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| ### Task 2: Implicit Causal Reasoning |
| Infer the latent emotional trigger from scattered context clues. The MCQ format helps reduce evaluator subjectivity. |
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| ### Task 3: Empathetic Response Generation |
| Evaluate generated responses in three dimensions: |
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| - **D1: Textual Empathy & Insight** (LLM-judge, 1-5) |
| - **D2: Vocal Empathy & Congruence** (human rating, 1-5) |
| - **D3: Audio Quality & Naturalness** (human rating, 1-5) |
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| ### Task 4: Acoustic-Semantic Conflict |
| Test robustness when text sentiment contradicts vocal affect (for example, sarcasm-like cases), exposing text-dominance bias. |
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| --- |
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| ## Key Findings |
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| - 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. |
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| --- |
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| --- |
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| ## Data and Code Access |
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| - The dataset is publicly available on HuggingFace: [HumDial-EIBench](https://huggingface.co/datasets/ASLP-lab/HumDial-EIBench) |
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| --- |
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| ## Evaluation Usage |
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| ### Task 3 (Empathetic Generation) Scoring |
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| `eval/eval_task3.py` scores model outputs for D1/D2/D3 and writes per-sample + summary results. |
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| #### Input format (`jsonl`) |
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| ```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" |
| } |
| ] |
| } |
| ``` |
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| #### Run |
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| ```bash |
| python eval/eval_task3.py \ |
| --model Qwen3-Omni-30B-A3B-Instruct \ |
| --input_file results/task3_outputs.jsonl \ |
| --output_file results/task3_scores.jsonl |
| ``` |
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| The script automatically identifies the target evaluation turn (second non-neutral turn) and builds context from prior turns. |
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| > Environment note: this script requires a GPU runtime and `vLLM`. Set the local judge checkpoint path in `eval/eval_task3.py` before running. |
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| --- |
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| --- |
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| ## Citation |
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| If you find this work useful, please cite: |
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| ```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}, |
| } |
| ``` |
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| ## License |
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| The code in this repository is released under the **Apache 2.0 License**. |
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| ## Contact |
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| - **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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