On the use of kernel PCA for feature extraction in speech recognition.
Amaro LimaHeiga ZenYoshihiko NankakuChiyomi MiyajimaKeiichi TokudaTadashi KitamuraPublished in: INTERSPEECH (2003)
Keyphrases
- speech recognition
- kernel pca
- feature extraction
- kernel principal component analysis
- principal component analysis
- pattern recognition
- dimensionality reduction
- face recognition
- feature space
- kernel methods
- hidden markov models
- speaker identification
- speech signal
- speech synthesis
- speech recognizer
- linear discriminant analysis
- language model
- noisy environments
- automatic speech recognition
- image processing
- kernel function
- spectral clustering
- image classification
- speech recognition systems
- cepstral coefficients
- feature vectors
- gabor wavelets
- wavelet transform
- discriminant analysis
- input space
- texture features
- kernel matrix
- extracted features
- low dimensional
- machine learning
- neural network
- support vector machine svm
- feature set
- face images
- support vector