Convolutional neural networks for pathological voice detection

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Abstract

Acoustic analysis using signal processing tools can be used to extract voice features to distinguish whether a voice is pathological or healthy. The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice. The novel classifier achieved 88.5%, 66.2% and 77.0% accuracy on training, validation and testing data set respectively on 482 normal and 482 organic dysphonia speech files. It reveals that the proposed novel algorithm on the Saarbruecken Voice Database can effectively been used for screening pathological voice recordings.
Original languageEnglish
Number of pages4
Publication statusPublished - 17 Jul 2018
Event40th International Conference of the IEEE Engineering in Medicine and Biology Society - Honolulu, Hawaii, United States
Duration: 17 Jul 201821 Jul 2018
https://embc.embs.org/2018/

Conference

Conference40th International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2018
CountryUnited States
CityHonolulu, Hawaii
Period17/07/1821/07/18
Internet address

Keywords

  • acoustic analysis
  • signal processing
  • pathological voice detection
  • healthy voice detection

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