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.

对书籍和有声书之间的93对数据集进行处理，从叙述性文本中使用语言建模预测出更好刻画人类有声阅读的语调、音量和说话速度的模型，通过与商业TTS系统的结果进行对比，发现预测的音调与人类朗读的相关性更高，并且预测的音量属性与人类朗读更为相似。最后，通过人类评估研究量化了人们对于语调增强型有声书朗读的偏好程度。

有声书的韵律分析