Waves, such as light and sound, inherently bounce and mix due to multiple scattering induced by the complex material objects that surround us. This scattering process severely scrambles the information carried by waves, challenging conventional communication systems, sensing paradigms, and wave-based computing schemes. Here, we show that instead of being a hindrance, multiple scattering can be beneficial to enable and enhance analog nonlinear information mapping, allowing for the direct physical implementation of computational paradigms such as reservoir computing and extreme learning machines. We propose a physics-inspired version of such computational architectures for speech and vowel recognition that operate directly in the native domain of the input signal, namely on real-sounds, without any digital pre-processing or encoding conversion and backpropagation training computation. We first implement it in a proof-of-concept prototype, a nonlinear chaotic acoustic cavity containing multiple tunable and power-efficient nonlinear meta-scatterers. We prove the efficiency of the acoustic-based computing system for vowel recognition tasks with high testing classification accuracy (91.4%). Finally, we demonstrate the high performance of vowel recognition in the natural environment of a reverberation room. Our results open the way for efficient acoustic learning machines that operate directly on the input sound, and leverage physics to enable Natural Language Processing (NLP).

翻译：波，例如光与声，由于我们周围复杂物质物体引起的多重散射而固有地发生反射和混合。这一散射过程严重扰乱波所携带的信息，对传统通信系统、传感范式以及基于波的计算方案构成挑战。在这里，我们证明多重散射非但不是障碍，反而有助于实现并增强模拟非线性信息映射，从而允许直接物理实现诸如储层计算和极限学习机等计算范式。我们提出一种基于物理启发的此类计算架构，用于语音和元音识别，该架构直接在输入信号的原始域（即真实声音）上运行，无需任何数字预处理或编码转换以及反向传播训练计算。我们首先在概念验证原型中实现它，该原型是一个包含多个可调谐且高效的非线性元散射体的非线性混沌声腔。我们证明了基于声学的计算系统在元音识别任务中的高效性，测试分类准确率高达91.4%。最后，我们展示了在混响室自然环境中元音识别的高性能。我们的结果为直接在输入声音上运行的高效声学学习机开辟了道路，并利用物理原理实现自然语言处理（NLP）。