Most of the current state-of-the-art speech recognition systems are based on HMMs which usually use mixture of Gaussian functions as state probability distribution model. It is a common practice to use EM algorithm for Gaussian mixture parameter learning. In this case, the learning is done in a ”blind”, data-driven way without taking into account how the speech signal has been produced and whic...

Background : Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important derive accurate measures of physical activity, behavior, sleep. We applied statistical learning approaches examine their promise detecting time compared the results with commonly wear (WT) algorithms. Methods Fifteen children, age...

In the synthesis part of a hidden Markov model (HMM) based speech synthesis system which we have proposed, a speech parameter vector sequence is generated from a sentence HMM corresponding to an arbitrarily given text by using a speech parameter generation algorithm. However, there is an inconsistency: although the speech parameter vector sequence is generated under the constraints between stat...

The problem of reducing a Hidden Markov Model (HMM) to one smaller dimension that exactly reproduces the same marginals is tackled by using system-theoretic approach. Realization theory tools are extended HMMs leveraging suitable algebraic representations probability spaces. We propose two algorithms return coarse-grained equivalent obtained stochastic projection operators: first returns models...

Lane-level map matching is essential for autonomous driving. In this paper, we propose a Hidden Markov Model (HMM) trajectory of noisy GPS measurements to the road lanes in which vehicle records its positions. To our knowledge, first time that HMM used lane-level matching. Apart from values, model further assisted by yaw rate data (converted lane change indicator signal) and visual cues form le...

Unconstrained human hand motions consisting of grasp motion and inhand manipulation lead to a fundamental challenge that many recognition algorithms have to face, in both theoretical and practical development, mainly due to the complexity and dexterity of the human hand. The main contribution of this thesis is a novel fuzzy framework of three proposed recognition algorithms. This consists of ex...

gesture and motion recognition are needed for a variety of applications. the use of human hand motions as a natural interface tool has motivated researchers to conduct research in the modeling, analysis and recognition of various hand movements. in particular, human-computer intelligent interaction has been a focus of research in vision-based gesture recognition. in this work, we introduce a 3-...

Spam deobfuscation is a processing to detect obfuscated words appeared in spam emails and to convert them back to the original words for correct recognition. Lexicon tree hidden Markov model (LTHMM) was recently shown to be useful in spam deobfuscation. However, LT-HMM suffers from a huge number of states, which is not desirable for practical applications. In this paper we present a complexity-...