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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Crossmixed convolutional neural network for digital speech recognition.

Quoc Bao Diep¹, Hong Yen Phan¹, Thanh-Cong Truong²

¹Faculty of Mechanical - Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City, Vietnam.

|April 26, 2024

Summary

This summary is machine-generated.

Convolutional neural networks (CNNs) offer advanced solutions for digital speech recognition, outperforming traditional methods. These models effectively learn complex audio features, enhancing accuracy and speed for applications like voice commands.

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Area of Science:

Artificial Intelligence
Machine Learning
Signal Processing

Background:

Digital speech recognition faces challenges in capturing complex audio signal characteristics like frequency, pitch, and timbre.
Traditional methods struggle with the intricate features required for accurate speech recognition.
Convolutional Neural Networks (CNNs) present a promising approach to address these limitations.

Purpose of the Study:

To introduce and evaluate three novel CNN-based models for digital speech recognition.
To demonstrate the superiority of CNNs over existing models in learning speech signal features.
To enhance the accuracy and efficiency of speech recognition systems.

Main Methods:

Development of three CNN architectures: 1D-CNN for direct data learning.
Implementation of 2D-CNN and 2DM-CNN utilizing Fourier transform for waveform-to-image conversion.
Training and testing on four large datasets comprising 30,000 samples each.

Main Results:

The proposed CNN models significantly outperformed established models like GoogLeNet and AlexNet.
Achieved high accuracy rates, with the best models reaching 95.87%, 99.65%, and 99.76%.
Demonstrated 5-10% performance improvement over other models in terms of accuracy and speed.

Conclusions:

The developed CNN models effectively learn complex speech features, leading to improved recognition.
These models offer enhanced accuracy and speed compared to existing speech recognition solutions.
The proposed approach holds potential for broad applications in virtual assistants, medical recording, and voice command systems.