Non-traditional prosodic features for automated phrase break prediction

Abstract

It is universally recognized that humans process speech and language in chunks, each meaningful in itself. Any two renditions or assimilations of a given sentence will exhibit similarities and discrepancies in the distribution of phrase breaks. Automated phrase break prediction assigns pauses to plain text as input, evaluated against human performance encapsulated in ‘gold standard’ boundary annotations in a speech corpus. This article advocates an enhanced feature set for phrase break prediction, incorporating non-traditional prosodic features. The authors have developed ProPOSEL, a prosody and part-of-speech English lexicon, as text annotation and text analytics tool. Application of ProPOSEL has so far uncovered a statistically significant correlation in English between certain sound patterns (i.e. the diphthongs and triphthongs of Received Pronunciation) and phrase breaks in very different genres. Thus, presence or absence of a complex vowel could easily be incorporated as an extra non-traditional classificatory feature in phrase break models. Our approach also suggests new possibilities for statistical analysis of texts, particularly authorship and genre, via favoured sound and rhythmic patterns in addition to lexis. Moreover, we suggest that our approach of text data-mining descriptive annotations of projected prosody for Text-to-Speech Synthesis and stylistic analysis is applicable to other languages.