The CARES corpus: a database of older adult actor simulated emergency dialogue for developing a personal emergency response system

  • Authors:
  • Victoria Young;Alex Mihailidis

  • Affiliations:
  • Intelligent Assistive Technology and Systems Lab, Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada and Institute of Biomaterials & Biomedical Eng ...;Intelligent Assistive Technology and Systems Lab, Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, Canada and Institute of Biomaterials & Biomedical Eng ...

  • Venue:
  • International Journal of Speech Technology
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

There has been limited research on automatic speech recognition systems developed specifically for older adults and there exist few older adult speech corpora available for training them. For our research, samples of primarily older adult voices within an emergency context were needed to help develop, train, and test the automatic speech recognition component of a novel, intelligent, speech-based personal emergency response system. We were unable to locate an existing speech corpus with all the properties we required. Specifically, these properties included spoken Canadian English, both male and female adult (especially older adult) speech, emotional or stressed speech, and emergency type dialogue. As a result, we created the Canadian adult regular and emergency speech (CARES) corpus. The goal of this paper will be to describe the design and development of the CARES corpus. The CARES corpus has been designed using information obtained from live emergency call centre call transcripts and research literature in the field of automatic speech recognition. This corpus consists of a collection of spontaneous speech, read sentences, simulated expression of words, phrases, and emergency scenarios from adult actors aged 23---91 years. The emphasis is on emergency type dialogue and older adult speech. A total of 40 participant voices are included in the corpus and over 70 % of the voices are from adults over the age of 50 years. Approximately 3,200 minutes of speech was acquired in total.