README.md

---
license: cc-by-nc-4.0
language:
- en
- es
- de
task_categories:
- automatic-speech-recognition
- text-to-speech
tags:
- medical-speech
- healthcare-ai
- clinical-documentation
- voice-ai
- speech-data
- asr
pretty_name: "Medical Speech Dataset"
dataset_info:
  features:
  - name: file_name
    dtype: string
  - name: id
    dtype: int64
  - name: gender
    dtype: string
  - name: ethnicity
    dtype: string
  - name: occupation
    dtype: string
  - name: birth_place
    dtype: string
  - name: mother_tongue
    dtype: string
  - name: dialect
    dtype: string
  - name: year_of_birth
    dtype: int64
  - name: years_at_birth_place
    dtype: int64
  - name: languages_data
    dtype: string
  - name: os
    dtype: string
  - name: device
    dtype: string
  - name: browser
    dtype: string
  - name: duration
    dtype: float64
  - name: emotions
    dtype: string
  - name: language
    dtype: string
  - name: location
    dtype: string
  - name: noise_sources
    dtype: string
  - name: script_id
    dtype: int64
  - name: type_of_script
    dtype: string
  - name: script
    dtype: string
  - name: transcript
    dtype: string
  - name: speaker_id
    dtype: string
configs:
- config_name: english_medical
  data_files:
  - split: medical
    path: english_medical/medical/**
- config_name: global_medical
  data_files:
  - split: medical
    path: global_medical/medical/**
size_categories:
- n<1K
---

# Medical Speech Dataset

**A specialized speech dataset for healthcare AI applications featuring real medical terminology, clinical conversations, and domain-specific vocabulary.**

This dataset is curated from the [complete-voiceai-speech-dataset](https://huggingface.co/datasets/SilencioNetwork/complete-voiceai-speech-dataset) and focuses specifically on medical domain speech data collected from real healthcare contexts.

## Dataset Overview

- **Total audio files**: 33 recordings
- **Total duration**: ~42 minutes
- **Languages**: English (native) + Global Medical (multilingual)
- **Domain**: Medical terminology, clinical documentation, patient-provider conversations
- **Audio format**: WAV files
- **Sample rate**: 48 kHz
- **License**: CC BY-NC 4.0 (free for research, non-commercial use)

## Target Applications

This dataset is designed for:

- **Medical ASR systems** (ambient clinical documentation, medical dictation)
- **Healthcare AI assistants** (Abridge, Suki, Nabla, Ambience Healthcare)
- **Medical voice note transcription**
- **Clinical conversation analysis**
- **Medical terminology recognition models**
- **Healthcare dialogue systems**

## Dataset Structure

```
medical-speech-dataset/
├── english_medical/
│   └── medical/
│       ├── data/           # 8 audio files
│       └── metadata.csv    # Speaker metadata
└── global_medical/
    └── medical/
        ├── data/           # 25 audio files
        └── metadata.csv    # Speaker metadata
```

## Data Splits

### English Medical (Native Speakers)
- **Files**: 8 recordings
- **Context**: Native English speakers discussing medical topics
- **Use case**: High-accuracy medical ASR training, US/UK clinical documentation

### Global Medical (Multilingual)
- **Files**: 25 recordings
- **Context**: Medical speech from diverse linguistic backgrounds
- **Use case**: Accent-robust medical ASR, global telehealth applications

## Key Features

✅ **Real medical terminology** - Conditions, medications, procedures, anatomical terms  
✅ **Natural speech patterns** - Disfluencies, hesitations, clinical conversation flow  
✅ **Diverse accents** - Global medical professionals and patients  
✅ **Domain-specific vocabulary** - Not available in general speech datasets  
✅ **Ethical data collection** - Consent-based, privacy-preserving

## Use Cases

### 1. Ambient Clinical Documentation
Train models to transcribe doctor-patient conversations in real-time (similar to Abridge, Suki, Nabla).

### 2. Medical Dictation Systems
Improve accuracy for physicians dictating clinical notes, discharge summaries, and prescriptions.

### 3. Telehealth Transcription
Build ASR systems for virtual healthcare consultations across diverse accents and languages.

### 4. Medical Voice Assistants
Develop voice-enabled healthcare tools for symptom checking, medication reminders, and patient education.

### 5. Clinical Research
Analyze speech patterns in medical contexts, study communication dynamics between providers and patients.

## Loading the Dataset

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("SilencioNetwork/medical-speech-dataset")

# Load specific split
english_medical = load_dataset("SilencioNetwork/medical-speech-dataset", data_dir="english_medical")
global_medical = load_dataset("SilencioNetwork/medical-speech-dataset", data_dir="global_medical")
```

## Sample Metadata

Each recording includes:
- `file_name`: Audio file identifier
- `birth_place`: Speaker's country/region of origin
- `language`: Primary language spoken
- `context`: Medical (clinical terminology, healthcare conversations)

## Medical Speech Characteristics

This dataset captures real-world medical speech features:
- **Medical jargon**: "hypertension", "myocardial infarction", "differential diagnosis"
- **Clinical abbreviations**: Spoken medical shorthand (BP, HR, PRN, etc.)
- **Provider-patient dynamics**: Turn-taking, clarification requests, empathy markers
- **Multilingual medical contexts**: Healthcare delivery across linguistic boundaries

## Ethical Considerations

All data was collected with explicit informed consent. No protected health information (PHI) is included - all recordings contain general medical terminology only, not patient-specific data.

## Need More Medical Speech Data?

This is a sample dataset from Silencio's larger Off-the-Shelf (OTS) medical speech inventory:

📊 **Available in full inventory:**
- 300+ hours of medical domain speech
- 15+ languages
- Specialized domains: cardiology, radiology, surgery, pharmacy, etc.
- Provider + patient perspectives

**Contact us for access**: [alex@silencioai.com](mailto:alex@silencioai.com)

## Citation

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

```bibtex
@dataset{silencio_medical_speech_2026,
  title={Medical Speech Dataset},
  author={Silencio Network},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/SilencioNetwork/medical-speech-dataset}
}
```

## Related Datasets

- [Complete Voice AI Speech Dataset](https://huggingface.co/datasets/SilencioNetwork/complete-voiceai-speech-dataset) - 39 language/accent variants
- [Indian Languages Speech](https://huggingface.co/datasets/SilencioNetwork/indian-languages-speech) - 9 Indian languages
- [European Languages Speech](https://huggingface.co/datasets/SilencioNetwork/european-languages-speech) - 5 European languages
- [Global English Accents Speech](https://huggingface.co/datasets/SilencioNetwork/global-english-accents-speech) - 20 English accent variants

## License

**CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International)

✅ Free for research and non-commercial use  
❌ Commercial use requires licensing (contact us)

## About Silencio

Silencio is a voice AI data sourcing company with 2M+ contributors across 180+ countries. We provide scaled sourcing of real-world audio and speech data for AI labs, robotics companies, and healthcare AI developers.

🌐 [silenciai.com](https://silencioai.com)  
📧 [sofia@silencioai.com](mailto:sofia@silencioai.com)  


---

**Tags**: medical speech, healthcare AI, clinical documentation, medical ASR, medical dictation, ambient scribe, domain-specific speech, medical terminology, healthcare NLP, voice health