# Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer

arXiv: https://arxiv.org/abs/2011.11436 · HTML: https://mlsanity.com/paper/2011.11436/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Zero-Shot Motor Health Monitoring by Blind Domain Transition](https://mlsanity.com/paper/2212.06154/) (2022)
- [Blind Restoration of Real-World Audio by 1D Operational GANs](https://mlsanity.com/paper/2212.14618/) (2022)
- [2D Self-Organized ONN Model For Handwritten Text Recognition](https://mlsanity.com/paper/2207.08139/) (2022)
- [Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery](https://mlsanity.com/paper/2312.10742/) (2023)
- [High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs](https://mlsanity.com/paper/2407.11040/) (2024)

## Abstract (arXiv)

Automatic classification of speech commands has revolutionized human computer interactions in robotic applications. However, employed recognition models usually follow the methodology of deep learning with complicated networks which are memory and energy hungry. So, there is a need to either squeeze these complicated models or use more efficient light-weight models in order to be able to implement the resulting classifiers on embedded devices. In this paper, we pick the second approach and propose a network layer to enhance the speech command recognition capability of a lightweight network and demonstrate the result via experiments. The employed method borrows the ideas of Taylor expansion and quadratic forms to construct a better representation of features in both input and hidden layers. This richer representation results in recognition accuracy improvement as shown by extensive experiments on Google speech commands (GSC) and synthetic speech commands (SSC) datasets.