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bhaskarvilles
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clinical-speech-samples

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Automatic Speech Recognition

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🏥 Clinical Speech Samples Dataset

High-Quality Clinical Audio Dataset for Medical AI Research

Curated dataset for training and evaluating clinical speech processing models

📖 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

Metric	Value
Total Samples	2,500 audio clips
Total Duration	~12 hours
Sample Rate	16 kHz
Format	WAV (16-bit PCM)
Languages	English
Speakers	150+ unique speakers
Environments	8 clinical settings

Split Distribution

Split	Samples	Duration	Purpose
Train	1,750 (70%)	~8.4 hours	Model training
Validation	375 (15%)	~1.8 hours	Hyperparameter tuning
Test	375 (15%)	~1.8 hours	Final evaluation

🗂️ 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

Examination Rooms - Private patient consultations
Emergency Department - High-noise, urgent care
Operating Rooms - Surgical team communications
ICU - Intensive care monitoring
Outpatient Clinics - Routine checkups
Telemedicine - Remote consultations
Medical Education - Teaching rounds
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:

Removal of all patient names and identifiers
Exclusion of specific medical record numbers
Generalization of rare conditions
Audio filtering of identifying information
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:

Model	SI-SNR (dB)	SDR (dB)	PESQ	STOI
Baseline	12.5	10.2	2.8	0.85
CSM (ours)	15.8	13.5	3.2	0.91
State-of-art	16.2	14.1	3.4	0.93

Metrics on test set for speech enhancement task

Benchmark Tasks

Speech Enhancement: Clean speech from noisy clinical audio
Source Separation: Separate multiple speakers
ASR Accuracy: Speech-to-text in medical context
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

Language: English only (US accents primarily)
Geography: US healthcare system context
Settings: Limited to 8 clinical environments
Speakers: 150 unique speakers (may not represent all demographics)
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

🙏 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.

🏥 Advancing Medical AI Through Quality Data

Built for researchers, by researchers

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

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Total file size:

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