Engineering
Automatic Speech Recognition
100%
Support Vector Machine
100%
Front End
50%
Learning Approach
50%
Speech Signal
50%
Linear Feature
50%
Learning System
50%
Noise Level
43%
Additive Noise
30%
Primary Goal
25%
Additive White Gaussian Noise
25%
Dimensionality
12%
Linear Processing
12%
Signal-to-Noise Ratio
5%
Classification Task
5%
Individual Subbands
5%
Computer Science
Speech Recognition
100%
Support Vector Machine
100%
Machine Learning Approach
50%
Linear Feature
50%
Speech Recognition System
25%
Linear Filtering
25%
cepstral
25%
Additive White Gaussian Noise
25%
Kernel Function
25%
Ensemble Method
12%
Individual Classifier
12%
Combined Classifier
12%
Individual Subbands
12%
Frequency Subbands
12%
Dimensionality Reduction
12%
Classification Task
12%
Signal-to-Noise Ratio
12%
Mel-Frequency Cepstral Coefficients
12%
