Pouria Fewzee

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Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are(More)
The number of speech features that are introduced to emotional speech recognition exceeds some thousands and this makes dimensionality reduction an inevitable part of an emotional speech recognition system. The elastic net, the greedy feature selection, and the supervised principal component analysis are three recently developed dimensionality reduction(More)
Feature extraction and dimensionality reduction may be found as the most imperative parts of emotional speech recognition problem. In this work, we propose a new set of speech features based on the distribution of energy in frequency domain. To investigate the applicability of the proposed model, we have set the first international audio/visual emotion(More)
Various regression models are used to predict the continuous emotional contents of social signals. The common trend to train those models is by minimizing a sense of prediction error or maximizing the likelihood of the training data. According to those optimization criteria, among two models, the one which results in a lower prediction error, or higher(More)
Proposed in this work is the notion of spectral emotion profile. The purpose of spectral emotion profile is to highlight the spectral differences of individuals in expressing emotions, and to make use of those differences towards personalizing recognition of emotions in speech. To define spectral emotion profile, we have taken advantage of the spectral(More)
This work proposes an approach for solving the linear regression problem by maximizing the dependence between prediction values and the response variable. The proposed algorithm uses the Hilbert-Schmidt independence criterion as a generic measure of dependence and can be used to maximize both nonlinear and linear dependencies. The algorithm is important in(More)
Emotional speech recognition is the problem of discriminating speech samples by their emotional content. Our solution to this problem lies on three areas: signal processing, computational linguistics, and statistical learning. At signal processing and computational linguistics levels, identification and extraction of speech features is concerned; proposed(More)
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