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Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry

  • We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.

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Metadaten
Author:Timo BaumannORCiDGND, Hussein Hussein, Burkhard Meyer-SickendiekORCiD
URL / DOI:https://aclanthology.org/W18-4505
Parent Title (English):Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL-2018), August 25, 2018, Santa Fe, New Mexico, USA
Publisher:Association for Computational Linguistics
Place of publication:Santa Fe, New Mexico
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2018
Release Date:2022/06/27
First Page:44
Last Page:49
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Information und Kommunikation
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG