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Authorship Analysis, also known as stylometry, has been an essential aspectof Natural Language Processing (NLP) for a long time. Likewise, the recentadvancement of Large Language Models (LLMs) has made authorship analysisincreasingly crucial for distinguishing between human-written and AI-generatedtexts. However, these authorship analysis tasks have primarily been focused onwritten texts, not considering spoken texts. Thus, we introduce the largestbenchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).HANSEN encompasses meticulous curation of existing speech datasets accompaniedby transcripts, alongside the creation of novel AI-generated spoken textdatasets. Together, it comprises 17 human datasets, and AI-generated spokentexts created using 3 prominent LLMs: ChatGPT, PaLM2, and Vicuna13B. Toevaluate and demonstrate the utility of HANSEN, we perform AuthorshipAttribution (AA) & Author Verification (AV) on human-spoken datasets andconducted Human vs. AI spoken text detection using state-of-the-art (SOTA)models. While SOTA methods, such as, character ngram or Transformer-basedmodel, exhibit similar AA & AV performance in human-spoken datasets compared towritten ones, there is much room for improvement in AI-generated spoken textdetection. The HANSEN benchmark is available at:https://huggingface.co/datasets/HANSEN-REPO/HANSEN.

HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis