Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution

Keyphrases

• Feature Map 100%
• Equal Error Rate 100%
• Depthwise Separable Convolution 100%
• Residual Network 100%
• Voice Spoofing Detection 100%
• Automatic Speech Recognition 66%
• Spoofing 66%
• Detection Accuracy 33%
• Order of Accuracy 33%
• Latency Constraint 33%
• Real-world Deployment 33%
• Model Complexity 33%
• Restricted Resources 33%
• Model Size 33%
• Single Model 33%
• Spectrogram 33%
• Ensemble Methods 33%
• Percentage Reduction 33%
• Voice Assistant 33%
• Skip Connection 33%
• U-Net 33%
• Deep Learning Model 33%
• Ensemble System 33%
• Compact System 33%
• Fake Data 33%
• MobileNet 33%
• Gradient-weighted Class Activation Mapping (Grad-CAM) 33%
• Lightweight CNN 33%
• Replay Attack Detection 33%
• Voice Spoofing Attack 33%
• Constant-Q Transform 33%

Computer Science

• Spoofing 100%
• Equal Error Rate 100%
• Residual Neural Network 100%
• Depthwise Separable Convolution 100%
• Feature Map 100%
• Speaker Verification 66%
• Detection Accuracy 33%
• Spoofing Attack 33%
• Model Complexity 33%
• Convolutional Neural Network 33%
• Voice Assistant 33%
• Deep Learning Model 33%
• Gradient-Weighted Class Activation Mapping 33%
• Ensemble Technique 33%