Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for $\sim 30\%$ of all numbers in the dataset and digits that start with the number 9 accounting for $\sim 5\%$ of all numbers in the dataset. This phenomenon, known as Benford's Law, is highly repeatable and appears in lists of numbers from electricity bills, stock prices, tax returns, house prices, death rates, lengths of rivers, and naturally occurring images. In this paper we demonstrate that human speech spectra also follow Benford's Law on average. That is, when averaged over many speakers, the frequencies of leading digits in speech magnitude spectra follow this distribution, although with some variability at the individual sample level. We use this observation to motivate a new set of features that can be efficiently extracted from speech and demonstrate that these features can be used to classify between human speech and synthetic speech.
Do What I Say: A Spoken Prompt Dataset for Instruction-Following
Karlsruhe Institute of Technology, Germany（卡尔斯鲁厄理工学院，德国）；Fondazione Bruno Kessler, Italy（布鲁诺·凯瑟尔基金会，意大利）；ACC Cyfronet AGH, Poland（AGH计算机科学与技术学院，波兰）；AGH University of Krakow, Poland（克拉科夫AGH大学，波兰）；Carnegie Mellon University, U.S.（卡内基梅隆大学，美国）
Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound
The Hong Kong University of Science（香港科学与技术大学）；Inner Mongolia University（内蒙古大学）；Beihang University（北京航空航天大学）；Queen Mary University of London（伦敦玛丽女王大学）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；University of Surrey（萨里大学）；University of Rochester（罗切斯特大学）；Independent Researcher（独立研究者）
