README.md

metadata

language:
  - zh
license: apache-2.0
task_categories:
  - automatic-speech-recognition
viewer: true
dataset_info:
  - config_name: User
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: duration
        dtype: float64
      - name: chat_id
        dtype: string
    splits:
      - name: train
        num_bytes: 889743413
        num_examples: 1008
      - name: eval
        num_bytes: 235780103
        num_examples: 253
  - config_name: Agent
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: text
        dtype: string
      - name: duration
        dtype: float64
      - name: chat_id
        dtype: string
    splits:
      - name: train
        num_bytes: 3749359214
        num_examples: 4552
      - name: eval
        num_bytes: 930931836
        num_examples: 1138
configs:
  - config_name: User
    data_files:
      - split: train
        path: User/train/*
      - split: eval
        path: User/eval/*
  - config_name: Agent
    data_files:
      - split: train
        path: Agent/train/*
      - split: eval
        path: Agent/eval/*
tags:
  - medical
  - healthcare
size_categories:
  - 1K<n<10K

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

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

⚠️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 yangxiao.wxy@antgroup.com 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.

🗂️ 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}, 
}