UncommonVoice: A crowdsourced dataset of dysphonic speech

Meredith Moore, Piyush Papreja, Michael Saxon, Visar Berisha, Sethuraman Panchanathan

Research output: Contribution to journalConference articlepeer-review

Abstract

To facilitate more accessible spoken language technologies and advance the study of dysphonic speech 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 spasmodic 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 freely and publicly available at www.uncommonvoice.org. In the following sections, we detail the data collection process as well as provide an initial analysis of the speech corpus.

Original languageEnglish (US)
Pages (from-to)2532-2536
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2020-October
DOIs
StatePublished - 2020
Event21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
Duration: Oct 25 2020Oct 29 2020

Keywords

  • Dataset human-computer interaction
  • Spasmodic dysphonia
  • Voice disorder

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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