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Free Verse and Beyond: How to Classify Post-modern Spoken Poetry

  • This paper presents the classification of rhythmical patterns detected in post-modern spoken poetry by means of machine learning algorithms that use manually engineered features or automatically learnt representations. We used the world's largest corpus of spoken poetry from our partner lyrikline. We identified nine rhythmical patterns within a spectrum raging from a more fluent to a more disfluent poetic style. The text data analyzed by a statistical parser. Prosodic features of rhythmical patterns are identified by using the parser information. For the classification of rhythmical patterns, we used a neural networks-based approach which use text, audio, and pause information between poetic lines as features. Different combinations of features as well as the integration of feature engineering in the neural networks-based approach are tested. We compared the performance of both approaches (feature-based and neural network-based) using combinations of different features. The results show – by using the weighted average of f-measure for the evaluation – that the neural networks-based approach performed much better in classification of rhythmical patterns. The important improvement of the classification results lies in the use of the audio information. The integration of feature engineering in the neural networks-based approach yielded a very small result improvement.

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Metadaten
Author:Hussein Hussein, Burkhard Meyer-SickendiekORCiD, Timo BaumannORCiDGND
DOI:https://doi.org/10.21437/SpeechProsody.2020-141
Parent Title (English):Proceedings of Speech Prosody: Tokyo, Japan, 25-28 May 2020
Publisher:ISCA
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2020
Release Date:2022/04/11
First Page:690
Last Page:694
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