Datasets:
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README.md
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### Dataset Summary
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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.
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Access to the dataset is regulated to ensure the confidentiality and ethical use of the sensitive data.
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To use the ChaLL dataset, you need to download it manually. Follow the instructions provided (**yet to be detailed**) for downloading the data. Once you have downloaded the files, please extract all files into a single folder.
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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.
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```shell
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### Data Splits
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## Dataset Creation
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### Curation Rationale
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The dataset was
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### Source Data
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#### Initial Data Collection and Normalization
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Audio
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#### Who are the source language producers?
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The
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### Annotations
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#### Annotation process
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#### Who are the annotators?
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### Personal and Sensitive Information
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The dataset contains audio recordings of
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## Considerations for Using the Data
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### Other Known Limitations
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## Additional Information
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### Citation Information
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### Contributions
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### Dataset Summary
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This dataset contains audio recordings of spontaneous speech by young learners of English in Switzerland.
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The recordings capture various language learning tasks designed to elicit authentic communication from the students.
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The dataset includes detailed verbatim transcriptions with annotations for errors made by the learners.
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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.
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Access to the dataset is regulated to ensure the confidentiality and ethical use of the sensitive data.
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To use the ChaLL dataset, you need to download it manually. Follow the instructions provided (**yet to be detailed**) for downloading the data. Once you have downloaded the files, please extract all files into a single folder.
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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.
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```shell
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### Data Splits
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#### `asr_acl`
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For the experiments in this paper, we split the dataset into five distinct folds of similar duration
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(about 16h each), where each class (and therefore also each speaker) occurs in only one fold.
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To simulate the use case of the ASR system being confronted with a new class of learners, each fold
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contains data from a mix of grades. The following figure visualises the duration and grade distribution of each fold.
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![Chall Folds](/chall_data_folds_v1.svg)
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## Dataset Creation
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### Curation Rationale
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The dataset was created to address the need for ASR systems that can handle children’s spontaneous speech
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and preserve their errors to provide effective corrective feedback in language learning environments.
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### Source Data
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#### Initial Data Collection and Normalization
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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.
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#### Who are the source language producers?
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The source data producers include primary school students from German-speaking Switzerland, aged 9 to 14 years, participating in language learning activities.
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### Annotations
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#### Annotation process
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The transcription and annotation process was outsourced to a transcription agency, following detailed guidelines for error annotation,
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including symbols for grammatical, lexical, and pronunciation errors, as well as German word usage.
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#### Who are the annotators?
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The annotators were professionals from a transcription agency, trained according to specific guidelines provided by the project team.
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### Personal and Sensitive Information
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The dataset contains audio recordings of minors.
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All data was collected with informed consent from legal guardians, and recordings are anonymized to protect the identities of the participants.
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## Considerations for Using the Data
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### Other Known Limitations
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The outsourcing of transcription and error annotations always poses a risk of yielding erroneous data, since most
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transcribers are not trained in error annotation.
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## Additional Information
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### Citation Information
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```bibtex
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@inproceedings{
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anonymous2024errorpreserving,
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title={Error-preserving Automatic Speech Recognition of Young English Learners' Language},
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author={Anonymous},
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booktitle={The 62nd Annual Meeting of the Association for Computational Linguistics},
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year={2024},
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url={https://openreview.net/forum?id=XPIwvlqIfI}
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}
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```
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### Contributions
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