This paper investigates different strategies allowing integration of contextual information during the feature extraction stage of a cursive handwriting HMM-based recognition system. First we propose to use linear discriminant analysis (LDA) in order to integrate the class information during feature set building. Secondly several zoning strategies are used to integrate local contextual informat...

Credit scoring has become a very important task as the credit industry has been experiencing double-digit growth rate during the past few decades. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the decision of network’s topology, importance of potential input variab...

One popular feature type in speech recognition is based on linear transformations of sequences of cepstral feature vectors. In general the transformation is generated in two steps: first a transformation like linear discriminant analysis (LDA) or heteroscedastic linear discriminant analysis (HLDA) is used to maximize separation between classes and reduce the dimensionality, followed by a decorr...

In this contribution we extend our previous work in two major directions: a) we analyze, through the use of Discriminant Analysis, the possibilities of using L-best local scores and N-best utterance hypotheses scores for utterance verification; b) we present experimental results not only for a spontaneously spoken natural number recognition task, as in [1], but also for a flexible large vocabul...

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We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech. Some of the key advancements that contribute to our system include: a nearest-neighbor discriminant analysis (NDA) approach (as opposed to LDA) for intersession variability compensation in the i-vector space, the application of speaker and channel-adapted features derived...

We present a comparison of speaker verification systems based on unsupervised and supervised mixtures of probabilistic linear discriminant analysis (PLDA) models. This paper explores current applicability of unsupervised mixtures of PLDA models with Gaussian priors in a total variability space for speaker verification. Moreover, we analyze the experimental conditions under which this applicatio...

We make a first investigation into a recently raised concern about the suitability of existing data analysis techniques when faced with the counter-intuitive properties of high dimensional data spaces, such as the phenomenon of distance concentration. Under the structural assumption of a generic linear model with a latent variable and an additive unstructured noise, we find that dimension reduc...

The interaction of consonantal and vocalic segments in FV syllables regarding identification of place of articulation of fricatives has been studied. A probabilistic model for integration of acoustic information in both segments is proposed. The model weights each segment’s contribution and integrates them in order to resemble listeners’ perception. First, the perceptual validity of the model h...

A novel transfer learning approach, referred to as Transfer Discriminant-Analysis of Canonical Correlations (Transfer DCC), is proposed to recognize human actions from one view (target view) via the discriminative model learned from another view (source view). To cope with the considerable change between feature distributions of source view and target view, Transfer DCC includes an effective no...