The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than the first-order hidden Markov model (HMM). The relationship among DHO-HMM, high-order HMM (HO-HMM), hidden semi-Markov model (HSMM), and HMM is presented and discussed. We derived recursive forward and backward probability functions for the partial observation sequenc...

The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than can the first-order hidden Markov model (HMM). The relations among the DHO-HMM, high-order HMM (HOHMM), hidden semi-Markov model (HSMM), and HMM are presented and discussed. Recursive forward and backward probability functions for the partial observation sequence were...

Stacked-Bottle-Neck (SBN) feature extraction is a crucial part of modern automatic speech recognition (ASR) systems. The SBN network traditionally contains a hidden layer between the BN and output layers. Recently, we have observed that an SBN architecture without this hidden layer (i.e. direct BN-layer – output-layer connection) performs better for a single language but fails in scenarios wher...

Hidden Markov models (HMMs) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and versatility in handling sequential signals. On the other hand, as these models have a complex structure, and also because the involved data sets usually contain uncertainty, it is di...

We use different types of training algorithms in the neural network. But, we cannot say which kind algorithm is fast for a given problem. So, this survey paper, are trying to find better categorization problems. For purpose, used ten pattern network MATLAB. Levenberg-Marquardt (LM), Bayesian regularization backpropagation (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Con...

Widely Speech Signal Processing has not been used much in the field of electronics and computers due to the complexity and variety of speech signals and sounds with the advent of new technology. However, with modern processes, algorithms, and methods which can proc Demand for speech recognition technology is expected to their mobile phones as all purpose lifestyle devices. In this paper, an imp...

I N T R O D U C T I O N This paper describes an iterative method for estimat ing the parameters of a hidden stochastic contextfree grammar (SCFG). The "hidden" aspect arises from the fact that ~ome information is not available when the grammar is trained. When a parsed corpus is used for training, production probabilities can be estimated by counting the number of times each production is used ...

Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this tends to convergence local optimum model parameters. Therefore, optimizing remains crucial challenging work. In paper, Variabl...

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...