  Data limitation is one of the most common issues in training machine learning
classifiers for medical applications. Due to ethical concerns and data privacy,
the number of people that can be recruited to such experiments is generally
smaller than the number of participants contributing to non-healthcare
datasets. Recent research showed that generative models can be used as an
effective approach for data augmentation, which can ultimately help to train
more robust classifiers sparse data domains. A number of studies proved that
this data augmentation technique works for image and audio data sets. In this
paper, we investigate the application of a similar approach to different types
of speech and audio-based features extracted from interactions recorded with
our automatic dementia detection system. Using two generative models we show
how the generated synthesized samples can improve the performance of a DNN
based classifier. The variational autoencoder increased the F-score of a
four-way classifier distinguishing the typical patient groups seen in memory
clinics from 58% to around 74%, a 16% improvement
