HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis

摘要

\textit{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 \textit{written texts}, not considering \textit{spoken texts}. Thus, we introduce the largest benchmark for spoken texts - {\sf HANSEN}(\underline{H}uman \underline{AN}d ai \underline{S}poken t\underline{E}xt be\underline{N}chmark). {\sf 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 {\sf HANSEN}, we perform Authorship Attribution (AA) & Author Verification (AV) on human-spoken datasets and conducted Human vs. AI text detection using state-of-the-art (SOTA) models. While SOTA methods, such as, character n-gram 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 {\sf HANSEN} benchmark is available at: https://huggingface.co/datasets/HANSEN-REPO/HANSEN