A speech command control-based recognition system for dysarthric patients based on deep learning technology

Keyphrases

• Automatic Speech Recognition 50%
• Command-and-control 100%
• Communication Device 25%
• Computational Power 25%
• Convolution Neural Network 25%
• Convolution Neural Network Model 25%
• Deep Learning Technology 100%
• Dysarthric 100%
• Experiment Results 25%
• Feature System 25%
• Implementation Cost 50%
• Mel Frequency Cepstral Coefficients 25%
• Mobile Devices 75%
• Model Size 25%
• Patient-centered 100%
• Patient-controlled 50%
• Phonetic Posteriorgrams 25%
• Power Requirement 25%
• Recognition System 100%
• Recognition-based 50%
• Small Animal Model 25%
• Speech Commands 100%
• Speech Features 25%
• Speech Recognition System 50%
• Voice Control 25%

Computer Science

• Communication Device 33%
• Convolution Neural Network Model 33%
• Convolutional Neural Network 33%
• Deep Learning Method 100%
• Learning Technology 100%
• Mel Frequency Cepstral Coefficient 33%
• Mobile Device 100%
• Power Requirement 33%
• Recognition System 100%
• Speech Recognition 66%
• Speech Recognition System 66%

Engineering

• Automatic Speech Recognition 100%
• Control Command 100%
• Convolutional Neural Network 50%
• Deep Learning Method 100%
• Implementation Cost 50%
• Network Model 25%
• Power Requirement 25%