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--- |
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language: |
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- en |
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pretty_name: "ChaLL" |
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tags: |
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- error-preservation |
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- sla |
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- children |
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license: "apache-2.0" |
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task_categories: |
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- automatic-speech-recognition |
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--- |
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# Dataset Card for ChaLL |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** https://github.com/mict-zhaw/chall_e2e_stt |
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- **Repository:** https://github.com/mict-zhaw/chall_e2e_stt |
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- **Paper:** tbd |
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- **Leaderboard:** |
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- **Point of Contact:** [email protected] |
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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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> [!IMPORTANT] |
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> <b>Data Availability</b>: The dataset that we collected contains sensitive data of minors and thus cannot be shared publicly. The |
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> data can, however, be accessed as part of a joint project with one or several of the original project |
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> partners, subject to a collaboration agreement (<b>yet to be detailed</b>). |
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To use the ChaLL dataset, you need to download it manually. |
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Once you have manually downloaded the files, please extract all files into a single folder. |
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You can then load the dataset into your environment using the following command: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset('chall', data_dir='path/to/folder/folder_name') |
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``` |
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Ensure the path specified in `data_dir` correctly points to the folder where you have extracted the dataset files. |
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Examples in this dataset are generated using the `soundfile` library (for reading and chunking). |
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To handle the audio data correctly, you need to install the soundfile library in your project. |
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```shell |
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pip install soundfile |
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``` |
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### Supported Tasks and Leaderboards |
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[More Information Needed] |
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### Languages |
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The primary language represented in this dataset is English, specifically as spoken by Swiss children who are learners of the language. |
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This includes a variety of accents and dialectal influences from the German-speaking regions of Switzerland. |
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## Dataset Structure |
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The dataset can be loaded using different configurations to suit various experimental setups. |
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### Dataset Builder Configuration |
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The configurations define how the data is preprocessed and loaded into the environment. |
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Below are the details of the configurations used in experiments: |
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#### `original` |
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This configuration uses the data in its raw, unmodified form while ensuring all participant information is anonymized. |
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It includes the preservation of the data's original structure without segmentation, filtering, or other preprocessing techniques. |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset('mict-zhaw/chall', 'original', data_dir='path/to/folder/folder_name') |
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``` |
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#### `asr` |
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This configuration is intended for ASR experiments, enabling segment splitting for more granular processing of the audio data. |
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#### `asr_acl` |
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This configuration includes specific settings used in the related research paper. |
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It is designed to handle various segmentation and preprocessing tasks to prepare the data. |
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The results for the paper were generated at a time when the data was not yet complete. |
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Thus, this dataset configuration comprises approximately 85 hours (excluding pauses between utterances) of spontaneous English speech recordings from young learners in Switzerland, collected from 327 distinct speakers in grades 4 to 6. |
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The dataset includes 45,004 individual utterances and is intended to train an ASR system that preserves learner errors for corrective feedback in language learning. |
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The configuration is set to split segments, with a maximum pause length of 12 seconds, maximum chunk length of 12 seconds, minimum chunk length of 0.5 seconds, |
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removes trailing pauses, converts text to lowercase and numbers to words. |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset('mict-zhaw/chall', 'asr_acl', data_dir='path/to/folder/folder_name') |
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``` |
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#### Custom |
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The `ChallConfig` class provides various parameters that can be customized: |
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- **split_segments (`bool`):** Whether to split the audio into smaller segments. |
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- **max_chunk_length (`float` or `None`)**: Maximum length of each audio chunk in seconds (used only if split_segments is True). |
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- **min_chunk_length (`float` or `None`)**: Minimum length of each audio chunk in seconds (used only if split_segments is True). |
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- **max_pause_length (`float` or None)**: Maximum allowable pause length within segments (used only if split_segments is True). |
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- **remove_trailing_pauses (`bool`)**: Whether to remove trailing pauses from segments (used only if split_segments is True). |
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- **lowercase (`bool`)**: Whether to convert all text to lowercase. |
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- **num_to_words (`bool`)**: Whether to convert numerical expressions to words. |
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- **allowed_chars (`set`)**: Set of allowed characters in the text. Automatically set based on the lowercase parameter. |
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- **special_terms_mapping (`dict`)**: Dictionary for mapping special terms to their replacements. |
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- **stratify_column (`str` or `None`)**: Column used for stratifying the data into different folds. |
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- **folds (`dict` or `None`)**: Dictionary defining the data folds for stratified sampling. |
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Custom configurations can be used alone or in combination with existing ones, |
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and they will overwrite predefined defaults. |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset('mict-zhaw/chall', data_dir='path/to/folder/folder_name', **kwargs) |
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dataset = load_dataset('mict-zhaw/chall', 'asr_acl', data_dir='path/to/folder/folder_name', **kwargs) |
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``` |
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### Data Instances |
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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. |
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Here is an example: |
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#### `split_segments == True` |
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When `split_segments` is set to True, the audio data is divided into utterances. |
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An utterance data instance includes the spoken text from one participant along with meta information such as school_grade, area_of_school_code, background_noise, and intervention. |
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The audio is present as byte array under audio. |
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```json |
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{ |
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"audio_id": "S004_A005_000", |
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"intervention": 4, |
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"school_grade": "6", |
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"area_of_school_code": 5, |
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"background_noise": false, |
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"raw_text": "A male or is it a female?", |
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"clear_text": "a male or is it a female", |
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"words": { |
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"start": [0.4099999964237213, 0.5600000023841858, 1.0399999618530273, 1.25, 1.3700000047683716, 1.5499999523162842, 1.6699999570846558], |
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"end": [0.5400000214576721, 1.0399999618530273, 1.25, 1.3700000047683716, 1.5499999523162842, 1.6699999570846558, 2.5399999618530273], |
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"duration": [0.1300000250339508, 0.47999995946884155, 0.21000003814697266, 0.12000000476837158, 0.1799999475479126, 0.12000000476837158, 0.8700000047683716], |
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"text": ["A", "male", "or", "is", "it", "a", "female?"] |
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}, |
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"audio": { |
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"path": false, |
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"array": [0, 0, 0, "...", 0, 0, 0], |
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"sampling_rate": 16000 |
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} |
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} |
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``` |
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#### `split_segments == False` |
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When `split_segments` is set to False, the audio remains intact and includes multiple turns with one or more speakers. |
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In this case, additional participant meta information is present, but speakers (from the transcript) and participants cannot be aligned and do not need to match in number. |
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This means the transcription agency may define more than one speaker for a single participant. |
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```json |
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{ |
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"audio_id": "S001_A046", |
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"intervention": 1, |
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"school_grade": "4", |
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"area_of_school_code": 2, |
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"raw_text": "If you could have-have any superpower, what would it be? I would choose to have invincibility because when I'm invincible, I can't die or get hurt by anyone and I think this concept is very cool...", |
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"clear_text": "if you could have have any superpower what would it be i would choose to have invincibility because when i'm invincible i can't die or get hurt by anyone and i think this concept is very cool...", |
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"participants": { |
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"estimated_l2_proficiency": [null, null], |
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"gender": ["M", "F"], |
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"languages": ["NNS", "NNS"], |
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"pseudonym": ["P033", "P034"], |
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"school_grade": [6, 6], |
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"year_of_birth": [2010, 2011] |
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}, |
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"background_noise": true, |
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"speakers": { |
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"name": ["Participant 1", "Participant 2"], |
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"spkid": ["S002_A004_SPK0", "S002_A004_SPK1"] |
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}, |
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"segments": { |
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"speaker": ["S002_A004_SPK0", "S002_A004_SPK1", ...], |
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"words": [ |
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{ |
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"start": [1.8799999952316284, 2.119999885559082, 3.2899999618530273, ...], |
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"end": [2.119999885559082, 2.390000104904175, 3.859999895095825, ...], |
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"duration": [0.2399998903274536, 0.2700002193450928, 0.5699999332427979, ...], |
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"text": ["If", "you", "could", "have-have", "any", "superpower,", "what", "would", "it", "be?"] |
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}, { |
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"start": [10.760000228881836, 11.029999732971191, 11.420000076293945, ...], |
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"end": [11.029999732971191, 11.420000076293945, 12.170000076293945, ...], |
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"duration": [0.26999950408935547, 0.3900003433227539, 0.75, ...], |
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"text": ["I", "would", "choose", "to", "have", "invincibility", "because", "when", "I'm", "invincible,", "I", "can't", "die", "or", "get", "hurt", "by", "anyone", "and", "I", "think", "this", "concept", "is", "very", "cool."] |
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} |
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] |
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}, |
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"audio": { |
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"path": null, |
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"array": [0, 0, 0, ..., 0, 0, 0], |
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"sampling_rate": 16000 |
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} |
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} |
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``` |
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### Data Fields |
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- **audio_id**: A unique identifier for the audio recording. |
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- **intervention**: An integer representing the type or stage of intervention. |
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- **school_grade**: The grade level of the student(s) involved in the recording. |
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- **area_of_school_code**: A code representing a specific area within the school. |
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- **raw_text**: The raw transcription of the audio, capturing exactly what was spoken. |
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- **clear_text**: A cleaned version of the raw text, formatted for easier analysis. |
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- **background_noise**: A boolean indicating whether background noise is present in the recording. |
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- **audio**: An object containing the audio data and related information. |
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- **path**: The file path of the audio recording (can be null). |
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- **array**: An array representing the audio waveform data. |
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- **sampling_rate**: The rate at which the audio was sampled, in Hz. |
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In addition to the common fields, there are specific fields depending on `split_segments`: |
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#### Utterance Data Instance (`True`) |
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- **words**: An object containing details about each word spoken in the utterance. |
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- **start**: A list of start times for each word. |
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- **end**: A list of end times for each word. |
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- **duration**: A list of durations for each word. |
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- **text**: A list of words spoken. |
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#### Audio Data Instance (`False`) |
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- **participants**: An object containing meta information about the participants in the recording. |
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- **estimated_l2_proficiency**: A list of estimated language proficiency levels. |
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- **gender**: A list of genders of the participants. |
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- **languages**: A list of languages spoken by the participants. |
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- **pseudonym**: A list of pseudonyms assigned to the participants. |
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- **school_grade**: A list of school grades for each participant. |
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- **year_of_birth**: A list of birth years for each participant. |
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- **speakers**: An object containing information about the speakers in the transcript. |
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- **name**: A list of speaker names as identified in the transcript. |
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- **spkid**: A list of speaker IDs. |
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- **segments**: An object containing details about each segment of the recording. |
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- **speaker**: A list of speaker IDs for each segment. |
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- **words**: A list of objects, each containing details about the words spoken in the segment. |
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- **start**: A list of start times for each word in the segment. |
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- **end**: A list of end times for each word in the segment. |
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- **duration**: A list of durations for each word in the segment. |
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- **text**: A list of words spoken in the segment. |
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### Data Splits |
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The data splits can define as part of the configuration using the `folds`. |
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Without specifying `folds` all data is loaded in the train split. |
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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](doc/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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### Social Impact of Dataset |
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The dataset supports the development of educational tools that could enhance language learning for children, providing an important resource for educational technology. |
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### Discussion of Biases |
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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. |
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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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### Dataset Curators |
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The dataset was curated by researchers at PHZH, UZH and Zhaw, with collaboration from local schools in Switzerland. |
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### Licensing Information |
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[More Information Needed] |
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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={Janick Michot, Manuela Hürlimann, Jan Deriu, Luzia Sauer, Katsiaryna Mlynchyk, Mark Cieliebak}, |
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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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Thanks to [@mict-zhaw](https://github.com/mict-zhaw) for adding this dataset. |