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Access Whissle Speech Simulated Medical Exams on Hugging Face
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Speech Simulated Medical Exams
Simulated patient-physician medical exam conversations with rich speech metadata annotations. Built for training single-step ASR models that transcribe and annotate multiple concepts simultaneously, including speaker changes, emotions, intents, and roles.
Dataset Details
| Property | Value |
|---|---|
| Examples | 25,706 |
| Language | English |
| Audio | 16 kHz WAV |
| Source | Simulated medical interviews (respiratory focus) |
Features
| Column | Description |
|---|---|
| Audio waveform | |
| Transcription with inline annotations (speaker role, emotion, intent, turn changes) |
Use Cases
- Medical ASR: Train speech recognition models specialized for clinical conversations
- Speaker diarization: Learn doctor vs. patient turn-taking patterns
- Emotion detection: Identify patient emotional states during medical consultations
- Intent classification: Classify clinical intents (symptoms, diagnosis, treatment)
Citation
Smith, Christopher William; Fareez, Faiha; Parikh, Tishya; Wavell, Christopher; Shahab, Saba; Chevalier, Meghan; et al. (2022). A dataset of simulated patient-physician medical interviews with a focus on respiratory cases. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5545842.v1
Published in Nature Scientific Data.
License
Whissle Inference-Only License 1.0. See LICENSE file for full terms.
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