Wed-2-1-8 UncommonVoice: A Crowdsourced Dataset of Dysphonic Speech

Meredith Moore(Arizona State University), Piyush Papreja(Arizona State University), Michael Saxon(Arizona State University), Visar Berisha(Arizona State University) and Sethuraman Panchanathan(Arizona State University)
Abstract: This paper presents UncommonVoice, a freely-available, crowd-sourced speech corpus consisting of 8.5 hours of speech from 57 individuals, 48 of whom have dysphonia. The speech material consists of non-words (prolonged vowels, and the prompt for diadochokinetic rate), sentences (randomly selected from TIMIT prompts and the CAPE-V intelligibility analysis), and spontaneous image descriptions. The data was recorded in a crowdsourced manner using a web-based application. This dataset is a fundamental resource for the development of voice-assistive technologies for individuals with dysphonia as well as the enhancement of the accessibility of voice-based technologies (automatic speech recognition, virtual assistants, etc). Research on articulation differences as well as how best to model and represent dysphonic speech will greatly benefit from a free and publicly available dataset of dysphonic speech. The dataset will be made available at http://uncommonvoice.org. In the following sections, we detail the data collection process as well as provide an initial analysis of the speech corpus.
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