A deep learning method for pathological voice detection using convolutional deep belief networks

Huiyi Wu, John Soraghan, Anja Lowit, Gaetano Di Caterina

Research output: Contribution to conferencePaperpeer-review

20 Citations (Scopus)
129 Downloads (Pure)


Automatically detecting pathological voice disorders such as vocal cord paralysis or Reinke’s edema is a challenging and important medical classification problem. While deep learning techniques have achieved significant progress in the speech recognition field there has been less research work in the area of pathological voice disorders detection. A novel system for pathological voice detection using convolutional neural network (CNN) as the basic architecture is presented in this work. The novel system uses spectrograms of normal and pathological speech recordings as the input to the network. Initially Convolutional deep belief network (CDBN) are used to pre-train the weights of CNN system. This acts as a generative model to explore the structure of the input data using statistical methods. Then a CNN is trained using supervised back-propagation learning algorithm to fine tune the weights. It will be shown that a small amount of data can be used to achieve good results in classification with this deep learning approach. A performance analysis of the novel method is provided using real data from the Saarbrucken Voice database
Original languageEnglish
Number of pages5
Publication statusPublished - 2 Sep 2018
EventInterspeech 2018 - Hyderabad, India
Duration: 2 Sep 20186 Sep 2018


ConferenceInterspeech 2018
Internet address


  • pathological voice detection
  • convolutional neural network (CNN)
  • convolutional deep belief network (CDBN)
  • deep learning

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