  In this paper, we exploit a Fully Convolutional Network (FCN) to analyze the
audio data of spontaneous speech for dementia detection. A fully convolutional
network accommodates speech samples with varying lengths, thus enabling us to
analyze the speech sample without manual segmentation. Specifically, we first
obtain the Mel Frequency Cepstral Coefficient (MFCC) feature map from each
participant's audio data and convert the speech classification task on audio
data to an image classification task on MFCC feature maps. Then, to solve the
data insufficiency problem, we apply transfer learning by adopting a
pre-trained backbone Convolutional Neural Network (CNN) model from the
MobileNet architecture and the ImageNet dataset. We further build a
convolutional layer to produce a heatmap using Otsu's method for visualization,
enabling us to understand the impact of the time-series audio segments on the
classification results. We demonstrate that our classification model achieves
66.7% over the testing dataset, 62.5% of the baseline model provided in the
ADReSS challenge. Through the visualization technique, we can evaluate the
impact of audio segments, such as filled pauses from the participants and
repeated questions from the investigator, on the classification results.
