  Recent advances in text-to-speech have made it possible to generate
natural-sounding audio from text. However, audiobook narrations involve
dramatic vocalizations and intonations by the reader, with greater reliance on
emotions, dialogues, and descriptions in the narrative. Using our dataset of 93
aligned book-audiobook pairs, we present improved models for prosody prediction
properties (pitch, volume, and rate of speech) from narrative text using
language modeling. Our predicted prosody attributes correlate much better with
human audiobook readings than results from a state-of-the-art commercial TTS
system: our predicted pitch shows a higher correlation with human reading for
22 out of the 24 books, while our predicted volume attribute proves more
similar to human reading for 23 out of the 24 books. Finally, we present a
human evaluation study to quantify the extent that people prefer
prosody-enhanced audiobook readings over commercial text-to-speech systems.
