This article describes an experiment in paratone detection based on a spoken corpus of English for Academic Purposes (EAP) recently automatically re-annotated with prosodic information. The Momel and INTSINT annotations were carried out using SPPAS. The EIIDA corpus was chosen as it offered long uninterrupted stretches of speech of academic presentations. We describe the clustering method adopted for automatic detection, contrasting a supervised and an unsupervised method of paratone boundary detection. We showcase the relevance of the annotation scheme followed for this corpus and contribute to the investigation of the phonostyle of lecture delivery. We discuss the relevance of clustering methods applied to the labels of the pitch targets for the analysis of paratones.
This paper discusses the prosodic properties of the paratone, the oral paragraph of speech. Using manually annotated paratones and automatic prosodic labels on the EIIDA corpus, we re-examine the claims proposed by Tench (1996). We show that rhythmic cues to signal paratone boundaries seem to be more reliable than absolute pitch values.
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