Automatic emotion recognition in speech is a research area with a wide range of applications in human interactions. The basic mathematical tool used for emotion recognition is Pattern recognition which involves three operations, namely, pre-processing, feature extraction and classification. This paper introduces a procedure for emotion recognition using Hidden Markov Models (HMM), which is used...

palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. texture is one of the most important features extracted from low resolution images. in this paper, a new local descriptor, local composition derivative pattern (lcdp) is proposed to extract smartly stronger...

In this paper, an introduction to hybrid modeling techniques for speech recognition is presented. A hybrid speech recognition system consists of the combination of Hidden Markov Models (HMMs) with Neural Networks (NNs) in order to combine the advantages of these two powerful pattern recognition techniques for improved speech recognition. An overview of several different hybrid speech recognitio...

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals o...

Data from human subjects recorded by an eyetracker while they are learning new faces suggests face recognition can be modeled as a sequential stochastic process, where the underlying observations at a given fixation depend on both foveal and parafoveal information. In contrast to most pattern recognition based approaches, this foveal face recognition approach is incremental and scalable to larg...

This paper focuses on the importance of global features for online character recognition. Global features represent the relationship between two temporally distant points in a handwriting pattern. For example, it can be defined as the relative vector of two xy-coordinate features of two temporally separated points. Most existing online character recognition methods do not utilize global feature...

The paper examines the application of semi-Markov models to the phenomenon of earthquakes in Tehran province. Generally, earthquakes are not independent of each other, and time and place of earthquakes are related to previous earthquakes; moreover, the time between earthquakes affects the pattern of their occurrence; thus, this occurrence can be likened to semi-Markov models. ...

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals o...

In the last years there has been increasing interest in developing discriminative training methods for hidden Markov models, with the aim to improve their performance in classification and pattern recognition tasks. Although several advances have been made in this area, they have been targeted almost exclusively to standard models whose conditional observations are given by a Gaussian mixture d...

This paper is presents a pattern recognition fusion method for isolated Malay digit recognition using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). The aim of the project is to increase the accuracy percentage of Malay speech recognition. This study proposes an algorithm for pattern recognition fusion of the recognition models. The endpoint detection, framing, normalization, Mel Fre...