Abstract

Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data instances, we treat each speaker's data as a probability distribution. We propose a simple non-parametric learning method to make predictions for out-of-sample speakers based on a probability distance measure to the speakers in the training set. This is in contrast to traditional learning methods that rely on Euclidean distances between individual instances. We evaluate the method on two pathological speech data sets with promising results.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5050-5054
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • Distribution regression
  • divergence
  • objective assessment
  • speech pathology

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Tu, M., Berisha, V., & Liss, J. (2017). Objective assessment of pathological speech using distribution regression. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 5050-5054). [7953118] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953118