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Dataset . 2022
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Dataset . 2022
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ZENODO
Dataset . 2022
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Introducing the COVID-19 YouTube (COVYT) speech dataset featuring the same speakers with and without infection

Authors: Andreas Triantafyllopoulos; Anastasia Semertzidou; Meishu Song; Florian B. Pokorny; Björn W. Schuller;

Introducing the COVID-19 YouTube (COVYT) speech dataset featuring the same speakers with and without infection

Abstract

The COVYT dataset contains speech samples from individuals who self-reported their COVID-19 infection on public social media platforms (YouTube, Xiaohongshu). These videos, as well as accompanying videos of the same people prior to infection, were mined in an attempt to gather publicly-available data for COVID-19 research. This release includes the links to the original videos along with the accompanying manual segmentation and diarisation that identifies the utterances of the target individuals. We are additionally releasing features derived from the segmented utterances. Finally, the dataset includes partitioning information according to 4 different cross-validation schemes. See the arxiv pre-print for more details: https://arxiv.org/abs/2206.11045

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Keywords

machine learning, speech dataset, computer audition, COVID-19, speech pathology, disease detection

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popularity
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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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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