HanselZz/MMedFD

MMedFD: A Real-World Healthcare Benchmark for Multi-Turn Full-Duplex Automatic Speech Recognition

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📄 Preprint: MMedFD — For the complete benchmark construction pipeline, evaluation methodology, dataset specifications, and additional implementation details, please refer to the preprint.

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⚠️Data Availability

Full access requires internal approval and a research-only data use agreement. 🚫 Non-Commercial Use This dataset is provided for non-commercial research and education only. Commercial use is prohibited. Researchers who wish to request full access may contact us with a brief description of their affiliation, project goals, intended use, and data protection plan. Only de-identified data may be shared, and redistribution is prohibited.

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🗂️ Data Release & Access

• —Public release (partial subset): We release only a portion of the data used for this benchmark’s training and evaluation. This Lite subset differs in amount and coverage from our internal full dataset and is not a drop-in replacement for the complete data.
• —What’s included: A reduced selection of dialogues/audio/text sufficient to reproduce the reported benchmark protocol at a smaller scale.
• —Not included: Additional sessions, higher-fidelity artifacts, and full validation/test coverage remain internal.

🔒 Privacy, Safety & Redaction

• —Privacy-preserving audio: To protect speaker privacy, all audio has been re-synthesized via TTS (privacy-preserving re-encoding). This process obfuscates speaker identity and acoustic biomarkers while preserving task-relevant linguistic content for modeling.
• —Real-world dialogs: The dialogue content originates from real-world collections. However, sensitive spans (e.g., direct identifiers, highly specific personal details) are automatically redacted by an LLM-based filter before release.
• —Residual risk: Despite these protections, re-identification attempts are prohibited. Please do not try to recover original identities or link samples to outside sources.

<!-- ## 📑 How to Cite

If this code or our benchmark is useful for your research, please consider citing our paper:

@misc{chen2025mmedfdrealworldhealthcarebenchmark,
      title={MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition}, 
      author={Hongzhao Chen and XiaoYang Wang and Jing Lan and Hexiao Ding and Yufeng Jiang and MingHui Yang and DanHui Xu and Jun Luo and Nga-Chun Ng and Gerald W. Y. Cheng and Yunlin Mao and Jung Sun Yoo},
      year={2025},
      eprint={2509.19817},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2509.19817}, 
}