task_categories:automatic-speech-recognition language:en license:cc-by-nc-sa-4.0 AAL AAVE Ebonics AAE Black English region:us
---
license: cc-by-nc-sa-4.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- AAL
- AAVE
- Ebonics
- AAE
- Black English
pretty_name: CORAAL Dataset
---

# Dataset Card for CORAAL

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This dataset comprises audio files, text files, and audio segments sourced from the Corpus of Regional African American Language (CORAAL).

CORAAL is a subset of the Online Resources for African American Language (ORAAL) project, initiated by a team of linguistics researchers at the University of Oregon.

The original CORAAL dataset encompasses over 220 sociolinguistic interviews featuring African American Language (AAL) speakers born between 1888 and 2005. Each interview includes accompanying audio files and human-transcribed transcripts.

While many large language models excel at automatic speech recognition, they often fall short when confronted with speech containing linguistic variations they haven't been trained on. Since CORAAL's initial release in January 2018 as the first public corpus of AAL data, it is highly probable that recent automatic speech recognition models struggle with AAL transcription.

The primary aim of this dataset is to facilitate developers in training or fine-tuning their ASR models specifically for AAL, "a language spoken by more than 30 million working-class African Americans across North America" (Wolfram). This effort ultimately seeks to enhance the inclusivity of everyday ASR technology.

For additional information regarding the original CORAAL dataset, please refer to the following links:

- [CORAAL Website](https://oraal.uoregon.edu/coraal)
- [CORAAL User Guide (PDF)](http://lingtools.uoregon.edu/coraal/userguide/CORAALUserGuide_current.pdf)

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)

### Citation Information

[More Information Needed]

### Contributions

Kendall, Tyler and Charlie Farrington. 2023. The Corpus of Regional African American Language. Version 2023.06. Eugene, OR: The Online Resources for African American Language Project. [https://doi.org/10.7264/1ad5-6t35].

Walt Wolfram. 2020. Urban African American Vernacular English.
In: Kortmann, Bernd & Lunkenheimer, Kerstin & Ehret, Katharina (eds.)
The Electronic World Atlas of Varieties of English.
None: None.
(Available online at http://ewave-atlas.org/languages/15, Accessed on 2023-09-26.)