In this Chapter we show that by considering eye movements, and in particular, the resulting sequence of gaze shifts, a stochastic process, a wide variety of tools become available for analyses and modelling beyond conventional statistical methods. Such tools encompass random walk analyses and more complex techniques borrowed from the pattern recognition and machine learning fields. After a brie...

Our goal here is to recognize a sign language measured from wearable sensor gloves. A sign language is expressed as a sequence of gestural patterns to convey a meaning. Hidden Markov models (HMMs) have been shown to be successful in temporal pattern recognition, such as speech, handwriting, and gesture recognition [4]. In this project, we investigate how well HMMs can perform when applied to si...

Second order hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (neural networks . . . ) are their capabilities to model noisy temporal signals of variable length. In a previous work, we proposed a new method based on second order hidden Markov models to learn and recognize places in an indoor en...

In this paper, we propose a method for estimating a score for English pronunciation. Scores estimated by the proposed method were evaluated by correlating them with the learner’s pronunciation score which was scored by native English teachers. The average correlation between the estimated pronunciation scores and the learner’s pronunciation scores over 1, 5, and 10 sentences was 0.807, 0.873, a...

The research within presents the use of Hidden Markov Models (HMM) for the detection of wireless devices in highly noisy environments using their unintended electromagnetic emissions (UEE). All electromagnetic devices emit such radiation that is unique to the electronics, housing, and other device attributes. This pattern recognition system can provide continuous detection analysis and can prov...

Pattern recognition is a well-established field of study and Optical Character Recognition (OCR) has long been seen as one of its important contributions. In this paper we describe the performances of a hybrid classification approach which combines both neural networks and hidden Markov models. This classification technique is dealing with features extracted through the wavelet transform method...

A new class of Support Vector Machine (SVM) that is applicable to sequential-pattern recognition such as speech recognition is developed by incorporating an idea of non-linear time alignment into the kernel function. Since the time-alignment operation of sequential pattern is embedded in the new kernel function, standard SVM training and classification algorithms can be employed without further...

Using social science methods to induce a state of frustration in users, we collected physiological , video and behavioral data, and developed a strategy for coupling these data with real-world events. The eeectiveness of the proposed strategy was tested in a study with thirty-six subjects , where the system was shown to reliably synchronize and gather data for aaect analysis. Hidden Markov Mode...

The success of many real-world applications demonstrates that hidden Markov models (HMMs) are highly effective in one-dimensional pattern recognition problems such as speech recognition. Research is now focussed on extending HMMs to 2-D and possibly 3-D applications which arise in gesture, face, and handwriting recognition. Although the HMM has become a major workhorse of the pattern recognitio...