TITLE: Augmented Datasheets for Speech Datasets and Ethical Decision-Making 

ABSTRACT: Speech datasets are crucial for training Speech Language Technologies (SLT);
however, the lack of diversity of the underlying training data can lead to
serious limitations in building equitable and robust SLT products, especially
along dimensions of language, accent, dialect, variety, and speech impairment -
and the intersectionality of speech features with socioeconomic and demographic
features. Furthermore, there is often a lack of oversight on the underlying
training data - commonly built on massive web-crawling and/or publicly
available speech - with regard to the ethics of such data collection. To
encourage standardized documentation of such speech data components, we
introduce an augmented datasheet for speech datasets, which can be used in
addition to "Datasheets for Datasets". We then exemplify the importance of each
question in our augmented datasheet based on in-depth literature reviews of
speech data used in domains such as machine learning, linguistics, and health.
Finally, we encourage practitioners - ranging from dataset creators to
researchers - to use our augmented datasheet to better define the scope,
properties, and limits of speech datasets, while also encouraging consideration
of data-subject protection and user community empowerment. Ethical dataset
creation is not a one-size-fits-all process, but dataset creators can use our
augmented datasheet to reflexively consider the social context of related SLT
applications and data sources in order to foster more inclusive SLT products
downstream.
Speech datasets are crucial for training Speech Language Technologies (SLT);
however, the lack of diversity of the underlying training data can lead to
serious limitations in building equitable and robust SLT products, especially
along dimensions of language, accent, dialect, variety, and speech impairment -
and the intersectionality of speech features with socioeconomic and demographic
features. Furthermore, there is often a lack of oversight on the underlying
training data - commonly built on massive web-crawling and/or publicly
available speech - with regard to the ethics of such data collection. To
encourage standardized documentation of such speech data components, we
introduce an augmented datasheet for speech datasets, which can be used in
addition to "Datasheets for Datasets". We then exemplify the importance of each
question in our augmented datasheet based on in-depth literature reviews of
speech data used in domains such as machine learning, linguistics, and health.
Finally, we encourage practitioners - ranging from dataset creators to
researchers - to use our augmented datasheet to better define the scope,
properties, and limits of speech datasets, while also encouraging consideration
of data-subject protection and user community empowerment. Ethical dataset
creation is not a one-size-fits-all process, but dataset creators can use our
augmented datasheet to reflexively consider the social context of related SLT
applications and data sources in order to foster more inclusive SLT products
downstream.
Augmented Datasheets for Speech Datasets and Ethical Decision-Making
http://arxiv.org/abs/2305.04672v1