Before considering a multiclass classification (one output neuron per narrator), we have more modestly operated a binary classification (one output neuron) to identify whether a given page belongs to Bill Hardy—the narrator the most evidently isolated by the k-means clustering. To be robust enough, the model must be trained on at least 4 of Bill Hardy’s pages (4% of the book) and 25 pages not belonging to Bill (25%). The way we used deep learning to classify Cain’s Jawbone pages is peculiar (using it as a pseudo-forensic tool to attach writings to a fictitious person) and nonoptimal (small dataset, therefore tiny training and test dataset; solution not officially recognized, potentially impacting the accuracy of the labels).