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Updated: May 6, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A lightweight ECA-based DCNN approach for speech command recognition.

Karthikeyan V¹, Saranya P¹, Natchiyar M¹

¹Dept. of ECE, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India.

Computers in Biology and Medicine

|August 24, 2025

Summary

This summary is machine-generated.

A new lightweight deep convolutional neural network with efficient channel attention (LW-DCNN-ECA) achieves high accuracy for speech recognition. This advanced model enhances communication and accessibility for users, including those with speech impairments.

Keywords:

Accuracy CNN Deep learning ECA Speech recognition

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Speech recognition technology facilitates audio stream transcription across various industries.
It serves as a biometric for user authentication and aids individuals with speech impairments.
The technology enables natural, human-like communication.

Purpose of the Study:

To propose a lightweight end-to-end deep convolutional neural network with an efficient channel attention framework (LW-DCNN-ECA) for improved speech recognition.
To enhance communication ease and flexibility through advanced speech recognition.

Main Methods:

A layer-modified end-to-end deep CNN incorporating an efficient channel attention (ECA) mechanism was developed.
The ECA layer is a computationally efficient module designed to boost the performance of lightweight deep convolutional neural networks.

Main Results:

The LW-DCNN-ECA model achieved 98.28% recognition rate and 0.5691 loss on the mini-speech commands dataset.
On the speech commands dataset, the model attained 99.98% recognition rate with 0.2634 loss.
Framework robustness was validated on the Fisher's corpus and CHiME-4 corpus.

Conclusions:

The proposed LW-DCNN-ECA framework demonstrates high efficacy and accuracy in speech recognition tasks.
This model offers a computationally efficient solution for enhancing speech recognition performance.
The technology holds significant potential for improving accessibility and communication tools.