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

Hussein Hussein, Burkhard Meyer-Sickendiek, Timo Baumann


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.


 DOI: 10.21437/SpeechProsody.2020-141

Cite as: Hussein, H., Meyer-Sickendiek, B., Baumann, T. (2020) Free Verse and Beyond: How to Classify Post-modern Spoken Poetry. Proc. 10th International Conference on Speech Prosody 2020, 690-694, DOI: 10.21437/SpeechProsody.2020-141.


@inproceedings{Hussein2020,
  author={Hussein Hussein and Burkhard Meyer-Sickendiek and Timo Baumann},
  title={{Free Verse and Beyond: How to Classify Post-modern Spoken Poetry}},
  year=2020,
  booktitle={Proc. 10th International Conference on Speech Prosody 2020},
  pages={690--694},
  doi={10.21437/SpeechProsody.2020-141},
  url={http://dx.doi.org/10.21437/SpeechProsody.2020-141}
}