We present the steps taken towards an exploration platform for a multi-modal corpus of German lyric poetry from the Romantic era developed in the project »textklang«. This interdisciplinary project develops a mixed-methods approach for the systematic investigation of the relationship between written text (here lyric poetry) and its potential and actual sonic realisation (in recitations, musical performances etc.). The multi-modal »textklang« platform will be designed to technically and analytically combine three modalities: the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of a musical setting of a poem. The methodological workflow will enable scholars to develop hypotheses about the relationship between textual form and sonic/prosodic realisation based on theoretical considerations, text interpretation and evidence from recorded recitations. The full workflow will support hypothesis testing either through systematic corpus analysis alone or with addtional contrastive perception experiments. For the experimental track, researchers will be enabled to manipulate prosodic parameters in (re-)synthesised variants of the original recordings. The focus of this paper is on the design of the base corpus and on tools for systematic exploration – placing special emphasis on our response to challenges stemming from multi-modality and the methodologically diverse interdisciplinary setup.

The application of artificial intelligence (AI) for text generation in creative domains raises questions regarding the credibility of AI-generated content. In two studies, we explored if readers can differentiate between AI-based and human-written texts (generated based on the first line of texts and poems of classic authors) and how the stylistic qualities of these texts are rated. Participants read 9 AI-based continuations and either 9 human-written continuations (Study 1, N=120) or 9 original continuations (Study 2, N=302). Participants’ task was to decide whether a continuation was written with an AI-tool or not, to indicate their confidence in each decision, and to assess the stylistic text quality. Results showed that participants generally had low accuracy for differentiating between text types but were overconfident in their decisions. Regarding the assessment of stylistic quality, AI-continuations were perceived as less well-written, inspiring, fascinating, interesting, and aesthetic than both human-written and original continuations.