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