MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition
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arXiv
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| Format: | Preprint |
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2025
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| author | Chen, Hongzhao Wang, XiaoYang Lan, Jing Ding, Hexiao Jiang, Yufeng Yang, MingHui Xu, DanHui Luo, Jun Ng, Nga-Chun Cheng, Gerald W. Y. Mao, Yunlin Yoo, Jung Sun |
| author_facet | Chen, Hongzhao Wang, XiaoYang Lan, Jing Ding, Hexiao Jiang, Yufeng Yang, MingHui Xu, DanHui Luo, Jun Ng, Nga-Chun Cheng, Gerald W. Y. Mao, Yunlin Yoo, Jung Sun |
| contents | Automatic speech recognition (ASR) in clinical dialogue demands robustness to full-duplex interaction, speaker overlap, and low-latency constraints, yet open benchmarks remain scarce. We present MMedFD, the first real-world Chinese healthcare ASR corpus designed for multi-turn, full-duplex settings. Captured from a deployed AI assistant, the dataset comprises 5,805 annotated sessions with synchronized user and mixed-channel views, RTTM/CTM timing, and role labels. We introduce a model-agnostic pipeline for streaming segmentation, speaker attribution, and dialogue memory, and fine-tune Whisper-small on role-concatenated audio for long-context recognition. ASR evaluation includes WER, CER, and HC-WER, which measures concept-level accuracy across healthcare settings. LLM-generated responses are assessed using rubric-based and pairwise protocols. MMedFD establishes a reproducible framework for benchmarking streaming ASR and end-to-end duplex agents in healthcare deployment. The dataset and related resources are publicly available at https://github.com/Kinetics-JOJO/MMedFD |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition Chen, Hongzhao Wang, XiaoYang Lan, Jing Ding, Hexiao Jiang, Yufeng Yang, MingHui Xu, DanHui Luo, Jun Ng, Nga-Chun Cheng, Gerald W. Y. Mao, Yunlin Yoo, Jung Sun Audio and Speech Processing Automatic speech recognition (ASR) in clinical dialogue demands robustness to full-duplex interaction, speaker overlap, and low-latency constraints, yet open benchmarks remain scarce. We present MMedFD, the first real-world Chinese healthcare ASR corpus designed for multi-turn, full-duplex settings. Captured from a deployed AI assistant, the dataset comprises 5,805 annotated sessions with synchronized user and mixed-channel views, RTTM/CTM timing, and role labels. We introduce a model-agnostic pipeline for streaming segmentation, speaker attribution, and dialogue memory, and fine-tune Whisper-small on role-concatenated audio for long-context recognition. ASR evaluation includes WER, CER, and HC-WER, which measures concept-level accuracy across healthcare settings. LLM-generated responses are assessed using rubric-based and pairwise protocols. MMedFD establishes a reproducible framework for benchmarking streaming ASR and end-to-end duplex agents in healthcare deployment. The dataset and related resources are publicly available at https://github.com/Kinetics-JOJO/MMedFD |
| title | MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.19817 |