Speech Recognition with Cochlea‐Inspired In‐Sensor Computing

Traditional speech recognition methods rely on software‐based feature extraction that introduces latency and high energy costs, making them unsuitable for low‐power devices. A proof‐of‐concept demonstration is provided of a bioinspired tonotopic sensor for speech recognition that mimics the human cochlea, using a spiral‐shaped elastic metamaterial. The measured modal response of the structure at different frequencies generates a spatially distributed signal, providing a spatiotemporal map of the input named “tonogram”. The device acts as an in‐sensor physical reservoir computing system, working simultaneously as a sensor and as a computing unit, capable of extracting features of spoken words relevant to speech recognition. Results indicate that this can serve as a valid alternative to traditional software‐based digital preprocessing, ensuring high accuracy in terms of classification, while reducing computational requirements. This work demonstrates the potential of bioinspired metamaterials for energy‐efficient auditory sensing and, beyond speech recognition, for applications such as IoT devices and edge computing artificial intelligence systems.