Kernel-Based Feature Extraction with a Speech Technology Application

Kernel-Based Feature Extraction and Speech Technology Applications

Speech Based Emotional Feature Extraction

With the increase in the computing capabilities of the microprocessors it has now become possible to do real time computations on speech signals and images, so automatic emotion recognition through speech signals has become an important area of research. In this paper we have discussed the methods of extracting emotional features from a regular speech signals. Index Terms —Emotional feature; fe...

Modified Kernel-based Nonlinear Feature Extraction

Feature Extraction (FE) techniques are widely used in many applications to pre-process data in order to reduce the complexity of subsequent processes. A group of Kernel-based nonlinear FE ( H E ) algorithms has attracted much attention due to their high per$ormance. However, a serious limitation that is inherent in these algorithms -the maximal number of features extracted by them is limited by...

SVM based feature extraction in speech synthesis

Annotations of speech recordings are a fundamental part of any unit selection speech synthesiser. However, obtaining flawless annotations is an almost impossible task. Manual techniques can achieve the most accurate annotations, provided that enough time is available to analyse every phone individually. Automatic annotation techniques are a lot faster than manual, doing the task in a much more ...

KNIFE: Kernel Iterative Feature Extraction

Selecting important features in non-linear or kernel spaces is a difficult challenge in both classification and regression problems. When many of the features are irrelevant, kernel methods such as the support vector machine and kernel ridge regression can sometimes perform poorly. We propose weighting the features within a kernel with a sparse set of weights that are estimated in conjunction w...