README.md

# Dataset Card for ChaLL

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://github.com/mict-zhaw/chall_e2e_stt
- **Repository:** https://github.com/mict-zhaw/chall_e2e_stt
- **Paper:** tbd
- **Leaderboard:**
- **Point of Contact:** [email protected]

### Dataset Summary

This dataset contains audio recordings of spontaneous speech by young learners of English in Switzerland.
The recordings capture various language learning tasks designed to elicit authentic communication from the students.
The dataset includes detailed verbatim transcriptions with annotations for errors made by the learners.
The transcripts were prepared by a professional transcription service, and each recording was associated with detailed metadata, including school grade, recording conditions, and error annotations.

#### Data Availability

> [!WARNING]  
> The dataset that we collected contains sensitive data of minors and thus cannot be shared publicly. The
> data can, however, be accessed as part of a joint project with one or several of the original project
> partners, subject to a collaboration agreement (**yet to be detailed**).

To use the ChaLL dataset, you need to download it manually.
Once you have manually downloaded the files, please extract all files into a single folder.
You can then load the dataset into your environment using the following command:

```python
from datasets import load_dataset
dataset = load_dataset('chall', data_dir='path/to/folder/folder_name')
```

Ensure the path specified in `data_dir` correctly points to the folder where you have extracted the dataset files.

Examples in this dataset are generated using the `soundfile` library (for reading and chunking).
To handle the audio data correctly, you need to install the soundfile library in your project.

```shell
pip install soundfile
```

#### Data Configuration

The 


### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

The primary language represented in this dataset is English, specifically as spoken by Swiss children who are learners of the language.
This includes a variety of accents and dialectal influences from the German-speaking regions of Switzerland.


## Dataset Structure

### Data Instances

A typical data instance in this dataset include an audio file, its full transcription, error annotations, and associated metadata such as the speaker's grade level and recording conditions.
Here is an example:

```json
{
  
}
```

### Data Fields

[More Information Needed]

### Data Splits

#### `asr_acl`

For the experiments in this paper, we split the dataset into five distinct folds of similar duration
(about 16h each), where each class (and therefore also each speaker) occurs in only one fold.
To simulate the use case of the ASR system being confronted with a new class of learners, each fold
contains data from a mix of grades. The following figure visualises the duration and grade distribution of each fold.

![Chall Folds](doc/chall_data_folds_v1.svg)

## Dataset Creation

### Curation Rationale

The dataset was created to address the need for ASR systems that can handle children’s spontaneous speech 
and preserve their errors to provide effective corrective feedback in language learning environments.


### Source Data

#### Initial Data Collection and Normalization

Audio data was collected from primary school students aged 9 to 14 years, performing language learning tasks in pairs, trios, or individually. The recordings were made at schools and universities, and detailed verbatim transcriptions were created by a transcription agency, following specific guidelines.


#### Who are the source language producers?

The source data producers include primary school students from German-speaking Switzerland, aged 9 to 14 years, participating in language learning activities.

### Annotations

#### Annotation process

The transcription and annotation process was outsourced to a transcription agency, following detailed guidelines for error annotation, 
including symbols for grammatical, lexical, and pronunciation errors, as well as German word usage.


#### Who are the annotators?

The annotators were professionals from a transcription agency, trained according to specific guidelines provided by the project team.

### Personal and Sensitive Information

The dataset contains audio recordings of minors.
All data was collected with informed consent from legal guardians, and recordings are anonymized to protect the identities of the participants.


## Considerations for Using the Data

### Social Impact of Dataset

The dataset supports the development of educational tools that could enhance language learning for children, providing an important resource for educational technology.

### Discussion of Biases

Given the specific demographic (Swiss German-speaking schoolchildren), the dataset may not generalize well to other forms of English or to speakers from different linguistic or cultural backgrounds.

### Other Known Limitations

The outsourcing of transcription and error annotations always poses a risk of yielding erroneous data, since most
transcribers are not trained in error annotation.

## Additional Information

### Dataset Curators

The dataset was curated by researchers at PHZH, UZH and Zhaw, with collaboration from local schools in Switzerland.

### Licensing Information

[More Information Needed]

### Citation Information

```bibtex
@inproceedings{
  anonymous2024errorpreserving,
  title={Error-preserving Automatic Speech Recognition of Young English Learners' Language},
  author={Anonymous},
  booktitle={The 62nd Annual Meeting of the Association for Computational Linguistics},
  year={2024},
  url={https://openreview.net/forum?id=XPIwvlqIfI}
}
```

### Contributions

Thanks to [@mict-zhaw](https://github.com/<github-username>) for adding this dataset.