  \emph{Topological data analysis} (TDA) has recently emerged as a new
technique to extract meaningful discriminitve features from high dimensional
data. In this paper, we investigate the possibility of applying TDA to improve
the classification accuracy of public speaking rating. We calculated
\emph{persistence image vectors} for the sentence embeddings of TEDtalk data
and feed this vectors as additional inputs to our machine learning models. We
have found a negative result that this topological information does not improve
the model accuracy significantly. In some cases, it makes the accuracy slightly
worse than the original one. From our results, we could not conclude that the
topological shapes of the sentence embeddings can help us train a better model
for public speaking rating.
