SPARSAR: An Expressive Poetry Reader

Abstract

We present SPARSAR, a system for the automatic analysis of poetry(and text) style which makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and taggers. In addition the system adds syntactic and semantic structural analysis and prosodic modeling. We do a dependency mapping to analyse the verbal complex and determine Discourse Structure. Another important component of the system is a phonological parser to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database of syllable durations; to account for missing syllables we built a syllable parser with the aim to evaluate durational values for any possible syllable structure. A fundamental component for the production of emotions is the one that performs affective and sentiment analysis. This is done on a line by line basis. Lines associated to specific emotions are then marked to be pronounced with special care for the final module of the system, which is reponsible for the production of expressive reading by a TTS module, in our case the one made available by Apple on their computers. Expressive reading is allowed by the possibility to interact with the TTS.