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One of the most important patterns in ancient as well as modern poetry is the enjambment, the continuation of a sentence beyond the end of a line, couplet, or stanza. The paper reports first activities towards the development of a digital tool to analyze the accentuation of poetic enjambments in readout poetry. The aim in this contribution is to recognize two forms of enjambment (emphasized and unemphasized) in poems using audio and text data. We use data from lyrikline which is a major online portal for spoken poetry whereas poems are read aloud by the original authors. We identified by hermeneutical means based on literary analysis a total of 69 poems being characteristic for the use of enjambments in modern and postmodern German poetry and train classifiers to differentiate the emphasized/unemphasized categorization. A remarkable result of our automated analyses (and to our knowledge the first data-driven analysis of this kind) is the identification of a cultural difference in the accentuation of enjambments: statistically speaking, poets from the former GDR tend to emphasize the enjambment, whereas poets from the FRG do not. We use features derived from speech-to-text alignment and statistical parsing information such as pause lengths, number of lines with verbs, and number of lines with punctuation. The best classification results, calculated by the F-measure, for the both types of enjambment (emphasized/unemphasized) is 0.69.

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After overcoming the traditional metrics, modern and postmodern poetry developed a large variety of 'free verse prosodies' that falls along a spectrum from a more fluent to a more disfluent and choppy style. We present a method, grounded in philological analysis and theories on cognitive (dis)fluency, to analyze this 'free verse spectrum' into six classes of poetic styles as well as to differentiate three types of poems with enjambments. We use a model for automatic prosodic analysis of spoken free verse poetry which uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We then analyze and fine-tune the model with a particular focus on enjambments and in two ways: we drill down on classification performance by analyzing whether the model focuses on similar traits of poems as humans would, specifically, whether it internally builds a notion of enjambment. We find that our model is similarly good as humans in finding enjambments; however, when we employ the model for classifying enjambment-dominated poem types, it does not pay particular attention to those lines. Adding enjambment labels to the training only marginally improves performance, indicating that all other lines are similarly informative for the model.

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