More Sound, More Soundness? Improving Authorship Attribution withPhonemes

This paper assesses whether turning written French poetry into a speech-oriented representation can improve the performance of authorship attribution methods. To this end, we develop a phonetic transcription system to automatically convert poems from six authors – including the disputed <em>Illuminations</em> of Rimbaud – into phonetic transcriptions, and adapt existing tools to ingest and process phonetic data. The output of this grapheme-to-phoneme task is then enriched with minimal prosodic cues, namely the creation of synthetic tokens based on punctuation and the addition of basic French <em>liaisons</em>. Using the same trigram features and classifier across all representations, we observe that moving from orthographic to phonetic transcriptions with a modest prosodic enrichment raises the F-score from 0.89 to 0.95, while reducing inter-author confusion. These results suggest that even lightweight speech-based features, produced with reproducible rules and open tools, can meaningfully enhance stylometric analysis of French verse and warrant further study for contested texts.