Abstract

Instrumental analysis of speech sometimes complements subjective evaluations in speech and language therapy; however, apart from elemental speech features such as pitch and formant statistics, higher dimensional spectral features are rarely used in practice because they are clinically uninterpretable. While these features are likely to somehow be related to clinical intervention, this relationship remains to be determined. This paper uses artificial recurrent neural networks to map high-dimensional spectral features into phonological features that are easily interpretable and provide fine-resolution information regarding articulation quality. The evaluation on a dysarthric speech data set shows strong correlation between the phonological feature measures and perceptual ratings. To increase clinical utility, we provide a new way to visualize phonological disturbances that provides clinicians with actionable information about intervention strategies.

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.
Pages5045-5049
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

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Keywords

  • clinical applications
  • phonological features
  • recurrent neural networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Jiao, Y., Berisha, V., & Liss, J. (2017). Interpretable phonological features for clinical applications. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 5045-5049). [7953117] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953117