The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). Speech signals are often corrupted with background noise and the changes in signal characteristics could be fast. These issues are especially important for robust speech recognition. Robustness is...

The paper presents successful experiments on combining two speaker recognition methods into a hybrid system. The rest branch of recognition is an innovative approach based on discrete wavelet-transform. The second one is classic, based on HTK and classication into voice and unvoiced segments. The hybrid solution outperforms both on a small test set. Further tests of this solution will be conduc...

Motion estimation in image sequences is undoubtedly one of the most studied problems because for many applications, going from video coding to pattern recognition, motion estimation is a fundamental tool. In this paper a new methodology which, by minimizing a specific potential function, determines for each image pixel its motion parameter set is presented. The approach is based on MRFs (Markov...

A new pattern matching method, Partly Hidden Markov Model, is proposed and applied to speech recognition. Hidden Markov Model, which is widely used for speech recognition, can deal with only piecewise stationary stochastic process. We solved this problem by introducing the modi ed second order Markov Model, in which the rst state is hidden and the second one is observable. In this model, not on...

ÐIn this paper, we present a technique using Markov models with spectral features for recognizing 2D shapes. We will analyze the properties of Fourier spectral features derived from closed contours of 2D shapes and use these features for 2D pattern recognition. We develop algorithms for reestimating parameters of hidden Markov models. To demonstrate the effectiveness of our models, we have test...

A data stream is an ordered sequence of training instances arriving at a rate that does not permit to permanently store them in memory and leads to the necessity of online learning methods when trying to predict some hidden target variable. In addition, concept drift often occurs, what means means that the statistical properties of the target variable may change over time. In this paper, we pre...

This paper examines three algorithms to recognize speaker’s emotion using the speech signals. Target emotions are happiness, sadness, anger, fear, boredom and neutral state. MLB(Maximum-Likelihood Bayes), NN(Nearest Neighbor) and HMM(Hidden Markov Model) algorithms are used as the pattern matching techniques. In all cases, pitch and energy are used as the features. The feature vectors for MLB a...

The first stage in many pattern recognition tasks is to generate a good set of features from the observed data. Usually, only a single feature space is used. However, in some complex pattern recognition tasks the choice of a good feature space may vary depending on the signal content. An example is in speech recognition where phone dependent feature subspaces may be useful. Handling multiple su...

In this paper we introduce a new Italian dataset consisting of simultaneous recordings of continuous speech and trajectories of important vocal tract articulators (i.e. tongue, lips, incisors) tracked by Electromagnetic Articulography (EMA). It includes more than 500 sentences uttered in citation condition by three speakers, one male (cnz) and two females (lls, olm), for approximately 2 hours o...