Detecting Alzheimer’s Disease Using Gated Convolutional Neural Network from Audio Data

Tifani Warnita, Nakamasa Inoue, Koichi Shinoda


We propose an automatic detection method of Alzheimer's diseases using a gated convolutional neural network (GCNN) from speech data. This GCNN can be trained with a relatively small amount of data and can capture the temporal information in audio paralinguistic features. Since it does not utilize any linguistic features, it can be easily applied to any languages. We evaluated our method using Pitt Corpus. The proposed method achieved the accuracy of 73.6%, which is better than the conventional sequential minimal optimization (SMO) by 7.6 points.


 DOI: 10.21437/Interspeech.2018-1713

Cite as: Warnita, T., Inoue, N., Shinoda, K. (2018) Detecting Alzheimer’s Disease Using Gated Convolutional Neural Network from Audio Data. Proc. Interspeech 2018, 1706-1710, DOI: 10.21437/Interspeech.2018-1713.


@inproceedings{Warnita2018,
  author={Tifani Warnita and Nakamasa Inoue and Koichi Shinoda},
  title={Detecting Alzheimer’s Disease Using Gated Convolutional Neural Network from Audio Data},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={1706--1710},
  doi={10.21437/Interspeech.2018-1713},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1713}
}