bhaskarvilles/clinical-speech-samples

🏥 Clinical Speech Samples Dataset

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High-Quality Clinical Audio Dataset for Medical AI Research

 ![Quality]() ![Privacy]()

Curated dataset for training and evaluating clinical speech processing models

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📖 Dataset Description

This dataset contains de-identified clinical audio samples for research and development of medical speech processing systems. It's designed to support training and evaluation of:

• —🎤 Speech enhancement in clinical settings
• —🔊 Audio source separation for medical environments
• —🗣️ Clinical speech recognition
• —📊 Medical dictation processing
• —🏥 Healthcare communication systems

Key Features

• —✅ HIPAA Compliant: All PHI removed
• —✅ High Quality: Professional medical-grade recordings
• —✅ Diverse: Multiple clinical scenarios and environments
• —✅ Annotated: Comprehensive metadata
• —✅ Research Ready: Pre-processed and standardized

📊 Dataset Statistics

Split Distribution

🗂️ Dataset Structure

File Organization

clinical-speech-samples/
├── train/
│   ├── clean/          # Clean speech samples
│   ├── noisy/          # Samples with clinical noise
│   └── mixed/          # Multi-speaker scenarios
├── validation/
│   └── [same structure as train]
├── test/
│   └── [same structure as train]
└── metadata/
    ├── speakers.csv    # Speaker demographics
    ├── scenarios.csv   # Clinical scenarios
    └── annotations.csv # Detailed annotations

Audio Characteristics

Clean Speech:

• —Single speaker
• —Minimal background noise (<35 dB SNR)
• —Clinical vocabulary and terminology

Noisy Speech:

• —Clinical environment sounds
• —Medical equipment noise
• —Background conversations
• —Realistic hospital acoustics

Mixed Speech:

• —Multiple simultaneous speakers
• —Doctor-patient interactions
• —Medical team communications

📋 Metadata

Each audio file includes comprehensive metadata:

{
  "file_id": "train_clean_0001",
  "duration": 15.3,
  "sample_rate": 16000,
  "speaker_id": "SPK_047",
  "gender": "F",
  "age_range": "35-45",
  "clinical_setting": "examination_room",
  "noise_level": "low",
  "scenario": "patient_consultation",
  "medical_specialty": "general_practice",
  "transcription_available": true
}

Clinical Settings

1. 1.Examination Rooms - Private patient consultations
2. 2.Emergency Department - High-noise, urgent care
3. 3.Operating Rooms - Surgical team communications
4. 4.ICU - Intensive care monitoring
5. 5.Outpatient Clinics - Routine checkups
6. 6.Telemedicine - Remote consultations
7. 7.Medical Education - Teaching rounds
8. 8.Administrative - Medical documentation

🎯 Intended Use

Primary Applications

✅ Research & Development:

• —Training speech enhancement models
• —Developing clinical ASR systems
• —Source separation algorithm testing
• —Medical AI system evaluation

✅ Medical Applications:

• —Clinical documentation automation
• —Telemedicine audio quality improvement
• —Medical dictation systems
• —Healthcare communication analysis

✅ Academic Research:

• —Published research in medical AI
• —Educational projects
• —Benchmark evaluations
• —Algorithm comparisons

Out-of-Scope Uses

❌ Prohibited Applications:

• —Patient identification or re-identification
• —Medical diagnosis without professional oversight
• —Surveillance or monitoring
• —Commercial use without proper licensing
• —Any use violating patient privacy

🔒 Privacy & Ethics

Data Collection

Privacy Protection:

• —All recordings fully de-identified per HIPAA
• —No Protected Health Information (PHI) included
• —IRB approval obtained for data collection
• —Informed consent from all participants
• —Professional voice actors for synthetic scenarios where appropriate

De-identification Process:

1. 1.Removal of all patient names and identifiers
2. 2.Exclusion of specific medical record numbers
3. 3.Generalization of rare conditions
4. 4.Audio filtering of identifying information
5. 5.Manual review by privacy experts

Ethical Considerations

Bias Mitigation:

• —Balanced gender representation
• —Multiple age groups included
• —Various medical specialties
• —Diverse clinical scenarios
• —Regional accent diversity

Limitations Acknowledged:

• —Predominantly English language
• —US healthcare system context
• —Limited to specific clinical settings
• —May not generalize to all medical environments

🚀 Getting Started

Loading the Dataset

Using Hugging Face Datasets

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("bhaskarvilles/clinical-speech-samples")

# Load specific split
train_data = load_dataset("bhaskarvilles/clinical-speech-samples", split="train")

# Access audio and metadata
for sample in train_data:
    audio = sample["audio"]
    metadata = sample["metadata"]
    print(f"Duration: {audio['array'].shape[0] / audio['sampling_rate']:.2f}s")

Manual Download

from huggingface_hub import hf_hub_download

audio_file = hf_hub_download(
    repo_id="bhaskarvilles/clinical-speech-samples",
    filename="train/clean/train_clean_0001.wav",
    repo_type="dataset"
)

Basic Usage Example

import torchaudio
from datasets import load_dataset

# Load dataset
dataset = load_dataset("bhaskarvilles/clinical-speech-samples", split="train")

# Process audio
for idx, sample in enumerate(dataset):
    waveform = sample["audio"]["array"]
    sr = sample["audio"]["sampling_rate"]
    
    # Your processing here
    # e.g., speech enhancement, ASR, source separation
    
    if idx >= 5:
        break

📊 Baseline Performance

Evaluation Metrics

Models trained on this dataset show the following performance:

Metrics on test set for speech enhancement task

Benchmark Tasks

1. 1.Speech Enhancement: Clean speech from noisy clinical audio
2. 2.Source Separation: Separate multiple speakers
3. 3.ASR Accuracy: Speech-to-text in medical context
4. 4.Noise Robustness: Performance across noise levels

🔧 Technical Specifications

Audio Format

• —Encoding: 16-bit PCM
• —Sample Rate: 16,000 Hz
• —Channels: Mono
• —Duration: 3-30 seconds per clip
• —File Format: WAV

Quality Control

All samples undergo:

• —✅ Amplitude normalization
• —✅ Silence trimming
• —✅ Quality assessment (SNR check)
• —✅ Metadata validation
• —✅ Privacy compliance review

Data Augmentation

For training, consider:

• —Time stretching (0.9x - 1.1x)
• —Pitch shifting (±2 semitones)
• —Additional noise injection
• —Room impulse response convolution
• —Speed perturbation

📚 Citation

If you use this dataset in your research, please cite:

@dataset{clinical_speech_samples_2025,
  author = {Bhaskar and Kerdos Infrasoft},
  title = {Clinical Speech Samples: A Dataset for Medical Audio AI},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/bhaskarvilles/clinical-speech-samples}},
  note = {De-identified clinical audio for speech processing research}
}

Related Publications

This dataset supports research published in:

• —Medical AI conferences (references to be added)
• —Healthcare technology journals
• —Speech processing workshops

🤝 Contributing

Feedback Welcome

We value your input:

• —🐛 Data Issues: Report problems in discussions
• —💡 Suggestions: Propose improvements
• —📊 Results: Share your findings
• —🤝 Collaboration: Research partnerships

Future Enhancements

Planned additions:

• —[ ] Additional clinical specialties
• —[ ] More diverse acoustic environments
• —[ ] Multilingual samples
• —[ ] Longitudinal recordings
• —[ ] Expanded metadata

📄 License

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to:

• —✅ Share — copy and redistribute
• —✅ Adapt — remix and transform
• —✅ Commercial use — with attribution

Under these terms:

• —📝 Attribution required
• —🔒 Respect privacy guidelines
• —⚖️ Comply with medical data regulations
• —🎯 Use for legitimate purposes only

Full License Text

⚠️ Limitations & Disclaimers

Known Limitations

1. 1.Language: English only (US accents primarily)
2. 2.Geography: US healthcare system context
3. 3.Settings: Limited to 8 clinical environments
4. 4.Speakers: 150 unique speakers (may not represent all demographics)
5. 5.Duration: Clips limited to 30 seconds maximum

Important Disclaimers

⚠️ Not for Medical Diagnosis: This dataset is for research and development only. Not approved for clinical diagnosis or treatment decisions.

⚠️ Validation Required: Any clinical application must undergo appropriate validation and regulatory approval.

⚠️ Privacy Compliance: Users must ensure compliance with local healthcare data regulations (HIPAA, GDPR, etc.).

🔗 Related Resources

Models

• —CSM - Clinical Speech Model
• —Medical ASR models (coming soon)

Datasets

• —Medical speech synthesis datasets
• —Clinical NLP corpora
• —Healthcare conversation datasets

Tools & Libraries

• —Asteroid - Audio source separation
• —SpeechBrain - Speech processing toolkit
• —ESPnet - End-to-end speech processing

📧 Contact

Dataset Maintainer

Created by: Bhaskar Organization: Kerdos Infrasoft Private Limited Email: ai@kerdos.xyz

Support

• —💬 Discussions
• —🐛 Report Issues
• —📚 Documentation

🙏 Acknowledgments

This dataset was created with support from:

• —Kerdos Infrasoft Private Limited
• —Clinical partners (anonymized for privacy)
• —Voice actors and participants
• —Medical AI research community

Special thanks to healthcare professionals who provided expertise and validation.

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🏥 Advancing Medical AI Through Quality Data

  

Built for researchers, by researchers

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Version: 1.0 Last Updated: January 6, 2025 Status: Active | Maintained Quality: Research Grade ⭐⭐⭐⭐⭐