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

Improving the automatic segmentation of subtitles through conditional random field

[EN] Automatic segmentation of subtitles is a novel research field which has not been studied extensively to date. However, quality automatic subtitling is a real need for broadcasters which seek for automatic solutions given the demanding European audiovisual legislation. In this article, a method based on Conditional Random Field is presented to deal with the automatic subtitling segmentation. This is a continuation of a previous work in the field, which proposed a method based on Support Vector Machine classifier to generate possible candidates for breaks. For this study, two corpora in Basque and Spanish were used for experiments, and the performance of the current method was tested and compared with the previous solution and two rule-based systems through several evaluation metrics. Finally, an experiment with human evaluators was carried out with the aim of measuring the productivity gain in post-editing automatic subtitles generated with the new method presented.This work was partially supported by the project CoMUN-HaT - TIN2015-70924-C2-1-R (MINECO/FEDER).Alvarez, A.; Martínez-Hinarejos, C.; Arzelus, H.; Balenciaga, M.; Del Pozo, A. (2017). Improving the automatic segmentation of subtitles through conditional random field. Speech Communication. 88:83-95. https://doi.org/10.1016/j.specom.2017.01.010S83958

Automatic Extraction of Quranic Lexis Representing Two Different Notions of Linguistic Salience: Keyness and Prosodic Prominence

This paper presents two sets of lexical items automatically extracted from the Arabic Qur’ān, and denoting two different notions of linguistic salience: keyness and prosodic prominence. Our novel hypothesis investigates a possible correlation between them. Our novel findings discover distributionally significant keywords that also occur strategically in phrase‐final position so as to maximise their prominence, and thus meaningfulness, for reader, reciter, and aural recipient. Our methodology first computes Quranic keywords via the Corpus Linguistics technique of Keyword Extraction, and maps them to major Quranic themes in Islamic scholarship. Next, we implement a bespoke algorithm for rule-based capture of words annotated with madd or prolongation, a specific type of prosodic highlighting in Quranic recitation rules or tajwīd. We find it especially interesting that the concept of final syllable lengthening (madd before pause) is encoded in tajwīid and effectively demarcates phrase boundaries in the Qur’ān. We concentrate on nominal keywords (i.e. nouns and adjectives) since these are more likely to be aligned with phrase edges and to bear the hallmarks of pre-boundary lengthening. This correlation between keyness and prominence occurs 43.29% of the time in our data, since 526 keywords appear in our extracted subset of nominal types tagged with madd before pause: ((526/1215)*100). Finally, we identify which Quranic keywords are most likely to be annotated with enhanced prolongation in the final syllable before pause, using an easy-to-interpret, single value metric: the Laplace Point Estimate. Keywords that emerge as semantically weighted in terms of both distributional and prosodic significance are most likely to reflect the Quranic themes of God, Nature, and Eschatology

Stochastic and Syntactic Techniques for Predicting Phrase Breaks

Determining the position of breaks in a sentence is a key task for a text-to-speech (TTS) system. We describe some meth-ods for phrase break prediction in which the whole sentence is considered, in contrast to most previous work which has fo-cused on using local features. Three approaches are described: by analogy, where the breaks from the best-matching sentence in our training data is used for the unseen sentence; by phrase modelling, in which we build stochastic models of phrases to segment unseen sentences; and nally, using features derived from a syntactic parse tree. Our best result, obtained on the MARSEC corpus and using a combination of parse derived fea-tures and a local feature, gave an F score of 81.6%, which we believe to be the highest published on this dataset. 1