In this paper a new variant of HMM named distributed VQ HMM (DVQHMM) is presented. Its main characteristic is the use of a code books distributed on HMM states with a new manner of HMM parameters estimation. Procedures for training and HMM evaluation of each recognition unit are described. Comparative results on an isolated phoneme recognition system are shown, between DVQHMM and conventional V...

In this paper, we evaluate performance of model adaptation by the previously proposed HMM decomposition method[1] on telephone speech recognition. The HMM decomposition method separates a composed HMM into a known phoneme HMM and an unknown noise and channel HMM by maximum likelihood (ML) estimation of the HMM parameters. A transfer function (telephone channel) HMM is estimated using adaptation...

<div class="WordSection1"><p>The purpose of this study is to discuss the design and implementation autonomous surface vehicle (ASV) systems. There’s a lot riding on advancement improvement ASV applications, especially given benefits they provide over other biometric approaches. Modern speaker recognition systems rely statistical models like hidden Markov model (HMM), support vector ...

Marine passive acoustic monitoring can be used to study biological, geophysical, and anthropogenic phenomena in the ocean. The wide range of characteristics from sounds sources makes simultaneous automatic detection classification these a significant challenge. Here, we propose single Hidden Markov Model-based system with Deep Neural Network (HMM-DNN) for low-frequency biological (baleen whales...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

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...