We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the methodology by introducing 16 biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for apparently unrelated diseases such as AD and COVID-19. Inspired by research conducted at the MIT Center for Brain Minds and Machines, OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. We apply it to AD, achieving above state-of-the-art accuracy of 93.8% on raw audio, while extracting a subject saliency map that longitudinally tracks relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model discriminating cough cultural origin. This cough dataset sets a new benchmark as the largest audio health dataset with 30,000+ subjects participating in April 2020, demonstrating for the first-time cough cultural bias.

翻译：我们引入了一个新型的音频处理架构,即开放语音脑模型(OVBM),提高阿尔茨海默氏(AD)自发言中纵向歧视的检测准确性。我们还概述了导致我们进入这种架构的OVBM设计方法,该设计方法一般可以同时纳入多式生物标志,同时针对几种疾病和其他AI任务。我们的方法之关键是使用多种生物标志互相补充,而当其中两个系统单独识别目标疾病中的不同主题时,我们称它们为正方形。我们通过引入16个生物标志来说明方法,其中3个是正形的,表明对阿尔茨海默氏(AD)和COVID-19等显然无关的疾病存在高于最新水平的直线直径歧视。我们将其应用到AD,在麻省脑智能和机器中心进行的研究中,OVBBM结合了四个智能模块的生物标志的实施:脑OS块和声音样本重叠,以及来自感官流和认知核心的生物标志特征,为目标任务创建了一个多式的直径直径直径的直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径网络网络网络网络。我们用了16次的直路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路。