---
language:
- fr
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- automatic-speech-recognition
pretty_name: PxCorpus
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: file_name
    dtype: string
  - name: transcription
    dtype: string
  - name: audio_name
    dtype: string
  - name: ner
    dtype: string
  - name: speaker_id
    dtype: int64
  - name: speaker_age_range
    dtype: string
  - name: speaker_gender
    dtype: string
  - name: speaker_category
    dtype: string
  - name: drug
    sequence: string
  - name: d_dos_val
    sequence: string
  - name: d_dos_up
    sequence: string
  - name: dur_val
    sequence: string
  - name: dur_ut
    sequence: string
  - name: dos_val
    sequence: string
  - name: dos_uf
    sequence: string
  - name: rhythm_tdte
    sequence: string
  - name: rhythm_perday
    sequence: string
  - name: inn
    sequence: string
  - name: d_dos_form
    sequence: string
  - name: freq_ut
    sequence: string
  - name: rhythm_hour
    sequence: string
  - name: dos_cond
    sequence: string
  - name: qsp_val
    sequence: string
  - name: qsp_ut
    sequence: string
  - name: cma_event
    sequence: string
  - name: roa
    sequence: string
  - name: A
    sequence: string
  - name: max_unit_val
    sequence: string
  - name: max_unit_ut
    sequence: string
  - name: max_unit_uf
    sequence: string
  - name: d_dos_form_ext
    sequence: string
  - name: rhythm_rec_ut
    sequence: string
  - name: fasting
    sequence: string
  - name: freq_int_v1
    sequence: string
  - name: freq_int_v1_ut
    sequence: string
  - name: re_val
    sequence: string
  - name: re_ut
    sequence: string
  - name: freq_val
    sequence: string
  - name: freq_int_v2
    sequence: string
  - name: rhythm_rec_val
    sequence: string
  - name: min_gap_ut
    sequence: string
  - name: freq_startday
    sequence: string
  - name: freq_int_v2_ut
    sequence: string
  - name: min_gap_val
    sequence: string
  - name: freq_days
    sequence: string
  - name: medical_terms
    sequence: string
  splits:
  - name: train
    num_bytes: 252725374.904
    num_examples: 1127
  - name: test
    num_bytes: 175599765.0
    num_examples: 570
  - name: dev
    num_bytes: 61546023.0
    num_examples: 283
  download_size: 465682214
  dataset_size: 489871162.90400004
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: dev
    path: data/dev-*
tags:
- medical
---

# PxCorpus : A Spoken Drug Prescription Dataset in French

PxCorpus is to the best of our knowledge, the first spoken medical drug prescriptions corpus to be distributed. 
It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in 
French acquired through an experiment with 55 participants experts and non-experts  in drug prescriptions.

The automatic transcriptions were verified by human effort and aligned with 
semantic labels to allow training of NLP models. The data acquisition protocol 
was reviewed by medical experts and permit free distribution without breach of 
privacy and regulation. 

## Overview of the Corpus
The experiment has been performed in wild conditions with naive participants and medical experts.
In total, the dataset includes 2067 recordings of 55 participants (38% non-experts,
25% doctors, 36% medical practitioners), manually transcribed and semantically annotated.

| Category         | Sessions | Recordings | Time(m)|
|------------------| -------- | ---------- | ------ |
| Medical experts  |   258    |    434     |  94.83 |
| Doctors          |   230    |    570     | 105.21 |
| Non experts      |   415    |    977     |  62.13 |
| Total            |   903    |   1981     | 262.27 |

## License
We hope that that the community will be able to benefit from the dataset  
which is distributed with an attribution 4.0 International (CC BY 4.0) Creative Commons licence.

## How to cite this corpus

If you use the corpus or need more details please refer to the following paper: A spoken drug prescription datset in French for spoken Language Understanding

@InProceedings{Kocabiyikoglu2022,
  author = 	"Alican Kocabiyikoglu and Fran{\c c}ois Portet and Prudence Gibert and Hervé Blanchon and Jean-Marc Babouchkine and Gaëtan Gavazzi",
  title = 	"A spoken drug prescription datset in French for spoken Language Understanding",
  booktitle = 	"13th Language Ressources and Evaluation Conference (LREC 2022)",
  year = 	"2022",
  location = 	"Marseille, France"
}


## Dataset features

* `path` -- Audio name
* `text` -- Audio utterance
* `ner` -- Semantic annotation from the original dataset
* `speaker_id` -- Speaker ID
* `speaker_age_range` -- Speaker age range
* `speaker_gender` -- Speaker gender
* `speaker_category` -- Speaker category (doctor, expert, non-expert)
* Other column names are for the occurences of each NER tag, could be useful for computing some metrics