Mobiusi

Chinese Audio Dataset for Doctor Instructions and Patient Calls for Help in Noisy ER Environments

#Voice semantic segmentation #noise environment voice recognition #medical instruction recognition #Intelligent healthcare #telemedicine systems #medical voice recognition
  • 500 hours
  • 1.2G
  • WAV
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

In modern healthcare systems, the noisy environment of emergency rooms poses significant challenges for voice communication between doctors and patients. Current voice recognition systems lack accuracy in such environments, severely affecting the practicality of smart medical devices. This dataset aims to address low accuracy of voice semantic segmentation in noisy conditions by collecting audio of doctor instructions and patient calls for help in ER settings. The collection method used high-sensitivity microphones recorded in real ER environments, and the data underwent multiple rounds of annotation and consistency checks, reviewed by a professional team with medical and acoustic backgrounds. Preprocessing steps include noise reduction, segmentation, and feature extraction. The data is stored in WAV format and organized by scenario and role.

Dataset Insights

Sample Examples

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Technical Specifications

FieldTypeDescription
file_namestringFile name
durationstringDuration
audio_ratestringAudio sample rate
audio_channelstringAudio channel
speaker_rolestringThe role of the speaker in the audio, such as doctor, nurse, or patient.
background_noise_typestringThe type of background noise in the audio, such as ambulance sounds, machinery, or chatter.
speech_claritystringThe clarity of the speech in the audio, such as clear or muffled.
emotional_tonestringThe emotional tone expressed by the speaker in the audio, such as urgent or calm.
languagestringThe language used in the audio, such as English or Spanish.
speech_speedstringThe speed of the speech in the audio, such as slow, medium, or fast.
command_or_callstringIndicates whether the audio contains a doctor's command or a patient's call for help.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the audio dataset for semantic segmentation of doctor instructions and patient calls in noisy emergency room environments?
This dataset consists of audio data collected in noisy emergency room environments for semantic segmentation of doctor instructions and patient calls, aiding intelligent medical systems in better recognizing speech commands and patient requests.
How does this dataset improve speech recognition performance in intelligent medical systems?
By performing semantic segmentation on the noisy audio data within the dataset, intelligent medical systems can more accurately extract and recognize critical speech commands and patient call information, thereby improving speech recognition performance.
In which medical scenarios is this dataset primarily applied?
This dataset is primarily applied in emergency rooms and other fast-response medical scenarios, assisting doctors in efficiently acquiring patient information and calling for support in noisy environments.
What are the main challenges of creating such a dataset?
The main challenges include recording clear audio in noisy emergency room environments, categorizing and annotating complex speech commands and call information, and ensuring the diversity and usability of the dataset.

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Cite this Work

@dataset{Mobiusi2026,
  title={Chinese Audio Dataset for Doctor Instructions and Patient Calls for Help in Noisy ER Environments},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/4854797093e5bb71f87f8296450fcfb8?dataset_scene_cate_type=3},
  urldate={2026-02-04},
  keywords={ER voice dataset, doctor instruction recognition, medical voice segmentation, noisy environment voice data},
  version={1.0}
}

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