On the Use of Kernel PCA for Feature Extraction in Speech Recognition.
Amaro LimaHeiga ZenYoshihiko NankakuChiyomi MiyajimaKeiichi TokudaTadashi KitamuraPublished in: IEICE Trans. Inf. Syst. (2004)
Keyphrases
- speech recognition
- kernel pca
- feature extraction
- kernel principal component analysis
- principal component analysis
- pattern recognition
- face recognition
- dimensionality reduction
- feature space
- hidden markov models
- kernel methods
- linear discriminant analysis
- language model
- automatic speech recognition
- feature vectors
- speech signal
- speech synthesis
- speech recognizer
- speaker identification
- spectral clustering
- discriminant analysis
- cepstral coefficients
- image processing
- support vector machine svm
- image classification
- noisy environments
- wavelet transform
- kernel matrix
- extracted features
- input space
- feature selection
- maximum likelihood
- component analysis
- texture features
- gabor wavelets
- feature set
- support vector machine
- classification accuracy
- high dimensional
- speech recognition systems