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Speech Recognition with Cochlea-Inspired In-Sensor Computing
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DOI：10.1002/aisy.202500526.png)
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Abstract
En 中文
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.
Keywords:
acoustics
bioinspiration
elastic metamaterials
in-sensor computing
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