Feature extraction is the key element when aiming at robust speech recognition. In this work both linear and nonlinear data-driven feature transformations were applied to the logarithmic mel-spectral context feature vectors in the TIMIT phone recognition task. Transformations were based on Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LD...

Linear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotate...

We previously developed noise robust Hierarchical SpectroTemporal (HIST) speech features. The learning of the features was performed in an unsupervised way with unlabeled speech data. In a final stage we deployed Principal Component Analysis (PCA) to reduce the feature dimensions and to diagonalize them. In this paper we investigate if a discriminant projection can further increase the performa...

In this paper we introduce Random Forest (RF) as a new modeling technique in the field of sensory analysis. As a case study we apply RF to the predictive discrimination of 6 typical cheeses of the Trentino province (North Italy) from data obtained by Quantitative Descriptive Analysis. The corresponding sensory profiling was carried out by 8 trained assessors using a developed language containin...

I-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) has become the state-of-the-art configuration for speaker verification. Recently, Gaussian-PLDA has been improved by a preliminary length normalization of i-vectors. This normalization, known to increase the Gaussianity of the i-vector distribution, also improves performance of systems based on standard Linear Discriminan...

Movement primitives or synergies have been extracted from human hand movements using several matrix factorization, dimensionality reduction, and classification methods. Principal component analysis (PCA) is widely used to obtain the first few significant eigenvectors of covariance that explain most of the variance of the data. Linear discriminant analysis (LDA) is also used as a supervised lear...

Many biometric applications such as face recognition involve data with a large number of features [1–3]. Analysis of such data is challenging due to the curse-ofdimensionality [4, 5], which states that an enormous number of samples are required to perform accurate predictions on problems with a high dimensionality. Dimensionality reduction, which extracts a small number of features by removing ...

This paper presents a study on statistical integration of temporal filter banks for robust speech recognition using linear discriminant analysis (LDA). The temporal properties of stationary features were first captured and represented using a bank of well-defined temporal filters. Then these derived temporal features can be integrated and compressed using the LDA technique. Experimental results...

A framework for automatic facial expression recognition combining Active Appearance Model (AAM) and Linear Discriminant Analysis (LDA) is proposed. Seven different expressions of several subjects, representing the neutral face and the facial emotions of happiness, sadness, surprise, anger, fear and disgust were analysed. The proposed solution starts by describing the human face by an AAM model,...