Modeling Pauses for Synthesis of Storytelling Style Speech Using Unsupervised Word Features

Abstract In the storytelling style speech pauses or phrase breaks play a significant role in introducing suspense and climax in the story. More often pauses are provided by a storyteller to capture the audience's attention by emphasizing keywords, focusing emotion-salient words, and to separate key phrases in an utterance. The goal of the work presented in this paper is to predict the location of pauses, in an utterance synthesized by a Story Text-To-Speech (TTS) system using unsupervised features at word-level. Traditional methods for predicting pauses uses the foremost linguistic features like Parts-of-Speech (POS) tags, chunking information or terminal syllables, etc. These methods presuppose the availability of linguistic knowledge by an automatic tagger or manually annotated corpus. However, this information's are not readily available in case of Indian Languages. Manually annotating the text with this linguistic information is quite hectic and time consuming. Also, these pieces of information's do not capture the co-occurrence statistics of words. Hence, we propose a framework for integrating the Story TTS with proposed pause prediction module. In this module, an unlabeled text corpus is used to extract, the continuous-valued world-level features to model the pause patterns in storytelling speech. A set of story-specific (SS) features are introduced for capturing story-semantic information based on pause pattern. A various combination of pause predictions systems is- proposed such as B, POS, U, POS+SS and U+SS. These systems are evaluated objectively by F-1 Score.