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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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Accent Classification Dataset

Authors: Chandresh Pravin; Varun Ojha;

Accent Classification Dataset

Abstract

This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland (NI), Scotland, Wales (SW), North East England (NE), North West England (NW), Yorkshire and Humber (YAH), East Midlands (EM), West Midlands (WM), East of England (EE), Greater London (GL), South East England (SE), South West England (SW). We split the data into a Male: Female ratio of 1:1, this is labelled with either '_M' for male or '_F' for female within the dataset. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks. The dataset also contains an unseen dataset of the Google opensource digit dataset, which contains audio files of the digits 1-9. This is included to test any models developed using the original dataset to confirm model performance to data variations.

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Keywords

Gender Classification, Accent Classification

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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