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Computer Science
Adversarial Machine Learning
100%
Amplitude Modulation
16%
Attention (Machine Learning)
38%
Behavior Analysis
22%
Convolutional Layer
11%
Convolutional Neural Network
42%
Deep Learning Model
19%
Deep Neural Network
29%
Detection Algorithm
11%
Discriminator
14%
Domain Adaptation
16%
Experimental Result
49%
Facial Expression
24%
Fusion Technique
22%
Gaussian Mixture Model
13%
Generative Adversarial Networks
14%
Generator Network
18%
Graphics Processing Unit
53%
Graphics Processor
33%
Individual Modality
37%
Intensity Level
13%
Language Identification
12%
Long Short-Term Memory Network
19%
Mel-Frequency Cepstral Coefficients
19%
Modern Processor
18%
Moore's Law
14%
Named Entity Recognition
11%
Noisy Environment
16%
Optimal Threshold
22%
Parallel Architectures
33%
Parallel Processing
14%
Parallelization
20%
Quotation Mark
11%
Recognition Accuracy
30%
Recurrent Network
33%
Residual Neural Network
22%
segmentation accuracy
22%
Speaker Recognition
22%
Speech Recognition
75%
Speech Recognition System
54%
Speech Recognizer
22%
Speed-up
17%
Supervised Learning
18%
Support Vector Machine
16%
Text To Speech
40%
Visual Feature
22%
Wavelet Transform
25%
Weakly Supervised Domain Adaptation
14%
Word Error Rate
16%
World Application
13%
Keyphrases
Adversarial Attack
22%
Adversarial Perturbation
16%
Advertiser
11%
Affective Behavior Analysis
14%
Attention Model
18%
Audio
31%
Audio Copy Detection
33%
Audio Features
16%
Audio-visual Fusion
33%
Broadcast News
14%
Closed Captions
16%
Concordance Correlation Coefficient
24%
Content-based
33%
Correction Module
11%
Cross-attention
24%
Cross-attention Module
14%
Cycle-consistent Adversarial Network
11%
Deep Neural Network
27%
Defense Approach
14%
Detection System
22%
Detector Response
11%
Dimensional Emotion Recognition
22%
Discrete Wavelet Transform
16%
Emotion Recognition
27%
Environmental Sound Classification
14%
Environmental Sounds
12%
Facial Affective Behavior
11%
Feature Representation
14%
Fusion Approach
16%
Fusion Model
25%
Graphics Processing Unit
20%
I-vector
22%
Input Signals
11%
Kaldi
11%
Modal
12%
Model Training
13%
Nearest Neighbor
33%
Parallel Architecture
11%
Real-time Correction
11%
Recognition Accuracy
16%
Spectrogram
24%
Speech Recognition
11%
Speech Recognition System
25%
Text-to-speech
16%
TRECVID
25%
Valence
35%
Valence-arousal Space
11%
Vocal
14%
Weakly Supervised Learning
11%
Word Error Rate
16%
