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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Analogue speech recognition based on physical computing.

Mohamadreza Zolfagharinejad¹, Julian Büchel², Lorenzo Cassola^1,3

¹NanoElectronics Group, MESA+ Institute and BRAINS Center for Brain-Inspired Computing, University of Twente, Enschede, the Netherlands.

|September 17, 2025

Summary

This summary is machine-generated.

We developed an efficient edge temporal-signal processor using in-materia computing. This system achieves high accuracy for speech commands with low power consumption, enabling advanced edge AI applications.

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Area of Science:

Computer Science
Materials Science
Electrical Engineering

Background:

Decentralized computing in edge devices (IoT, autonomous driving, healthcare) requires efficient, low-power processing of time-dependent signals.
Traditional processors face limitations due to the von Neumann bottleneck and domain conversions, hindering edge system performance.
In-materia computing offers a novel approach to overcome these limitations by performing computation within the material itself.

Purpose of the Study:

To propose and demonstrate an edge temporal-signal processor utilizing in-materia computing for efficient feature extraction and classification.
To achieve near-software accuracy on benchmark speech datasets with significantly reduced latency and power consumption.
To advance the development of compact, efficient, and high-performance heterogeneous smart edge processors.

Main Methods:

Developed a nonlinear, room-temperature reconfigurable-nonlinear-processing-unit layer for analogue, time-domain feature extraction from raw audio signals.
Implemented a compact analogue in-memory computing chip with memristive crossbar arrays for neural network classification.
Trained and evaluated the system on the TI-46-Word and Google Speech Commands datasets.

Main Results:

Achieved near-software accuracy for speech command recognition on benchmark datasets.
Demonstrated submillisecond latency for the complete processing pipeline.
Reported low energy consumption: ~300 nJ for feature extraction and ~78 µJ for classification (with potential down to ~10 µJ).

Conclusions:

The proposed in-materia computing-based edge temporal-signal processor offers a promising solution for efficient edge AI.
The system's high accuracy, low latency, and minimal power consumption pave the way for advanced, heterogeneous smart edge devices.
This work highlights the potential of in-materia computing to revolutionize edge processing for time-dependent signals.