Exploring complex vowels as phrase break correlates in a corpus of English speech with ProPOSEL, a prosody and POS English lexicon

Real-world knowledge of syntax is seen as integral to the machine learning task of phrase break prediction but there is a deficiency of a priori knowledge of prosody in both rule-based and data-driven classifiers. Speech recognition has established that pauses affect vowel duration in preceding words. Based on the observation that complex vowels occur at rhythmic junctures in poetry, we run significance tests on a sample of transcribed, contemporary British English speech and find a statistically significant correlation between complex vowels and phrase breaks. The experiment depends on automatic text annotation via ProPOSEL, a prosody and part-of-speech English lexicon. Copyright © 2009 ISCA

Complex vowels as boundary correlates in a multi-speaker corpus of spontaneous English speech

We have found empirical evidence of a correlation in English between words containing complex vowels (diphthongs and triphthongs) and ‘gold-standard’ phrase break annotations in datasets as apparently different as seventeenth-century verse and a Reith lecture transcript on economics from the late twentieth-century. Spontaneous speech in the form of BBC radio news reportage from the 1980s again exhibits this statistically significant correlation for five out of ten speakers, leading to speculation as to why speakers should fall into two distinct groups. The experiment depends on the automatic annotation of text with a priori knowledge from ProPOSEL, a prosody and part-of-speech English lexicon

ProPOSEC: A Prosody and PoS Annotated Spoken English Corpus

We have previously reported on ProPOSEL, a purpose-built Prosody and PoS English Lexicon compatible with the Python Natural Language ToolKit. ProPOSEC is a new corpus research resource built using this lexicon, intended for distribution with the Aix-MARSEC dataset. ProPOSEC comprises multi-level parallel annotations, juxtaposing prosodic and syntactic information from different versions of the Spoken English Corpus, with canonical dictionary forms, in a query format optimized for Perl, Python, and text processing programs. The order and content of fields in the text file is as follows: (1) Aix-MARSEC file number; (2) word; (3) LOB PoS-tag; (4) C5 PoS-tag; (5) Aix SAM-PA phonetic transcription; (6) SAM-PA phonetic transcription from ProPOSEL; (7) syllable count; (8) lexical stress pattern; (9) default content or function word tag; (10) DISC stressed and syllabified phonetic transcription; (11) alternative DISC representation, incorporating lexical stress pattern; (12) nested arrays of phonemes and tonic stress marks from Aix. As an experimental dataset, ProPOSEC can be used to study correlations between these annotation tiers, where significant findings are then expressed as additional features for phrasing models integral to Text-to-Speech and Speech Recognition. As a training set, ProPOSEC can be used for machine learning tasks in Information Retrieval and Speech Understanding systems