We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-Welch algorithm used in speech recognition. We also compare the accuracy and degree of sparsity of the new discriminative GMM classifier with those of generative GMM classifiers, and of kernel classifiers, such as suppo...

In this paper, we are estimating the pitch of telephone speech signal. We use different types of methods such us, Burg, Covariance, Fast Fourier transform, Modified Covariance, Multiple Signal Classification (MUSIC) algorithm or Eigen vector, Multi Taper method (MTM), Welch, and Yule Auto Regressive (Yule AR) , to estimate the PSD using signal processing tool box of MATLAB. The spectrum was con...

This paper explores the use of discrete-time recurrent neural networks for part-of-speech disambiguation of textual corpora. Our approach does not need a handtagged text for training the tagger, being probably the first neural approach doing so. Preliminary results show that the performance of this approach is, at least, similar to that of a standard hidden Markov model trained using the Baum-W...

A novel two-channel algorithm is proposed in this paper for discriminative training of Hidden Markov Models (HMMs). It adjusts the symbol emission coefficients of an existing HMM to maximize the separable distance between a pair of confusable training samples. The method is applied to identify the visemes of visual speech. The results indicate that the two-channel training method provides bette...

We present an efficient maximum likelihood (ML) training procedure for Gaussian mixture continuous density hidden Markov model (CDHMM) parameters. This procedure is proposed using the concept of approximate prior evolution, posterior intervention and feedback (PEPIF). In a series of experiments for training CDHMMs for a continuous Mandarin Chinese speech recognition task, the new PEPIF procedur...

As compared to many other techniques used in natural language processing, hidden markov models (HMMs) are an extremely flexible tool and has been successfully applied to a wide variety of stochastic modeling tasks. This paper uses a machine learning approach to examine the effectiveness of HMMs on extracting information of varying levels of structure. A stochastic optimization procedure is used...

We present a new type of HMM for vowel-to-consonant (VC) and consonant-to-vowel (CV) transitions based on the locus theory of speech perception. The parameters of the model can he trained automatically using the Baum-Welch algorithm and the training procedure does not require that instances of all possible CV and VC pairs be present. When incorporated into an isolated word recognizer with a 75 ...

In this paper, we consider a generalization of the state-of-art discriminative method for optimizing the conditional likelihood in Hidden Markov Models (HMMs), called the Extended Baum-Welch (EBW) algorithm, that has had significant impact on the speech recognition community. We propose a generalized form of EBW update rules that can be associated with a weighted sum of updated and initial mode...

In this paper we address the problem of searching in LZW compressed text directly, and present a new algorithm for finding multiple patterns by simulating the move of the Aho-Corasick pattern matching machine. The new algorithm finds all occurrences of multiple patterns whereas the algorithm proposed by Amir, Benson, and Farach finds only the first occurrence of a single pattern. The new algori...

bahrakan region is one of the four important and major areas of oil and economy in the persian gulf. the aim of this study was to evaluate the use of meiofauna as bioindicator of environmental pollution and the influence of organic contaminants on variation of meiofauna. sampling has done from 9 stations in three transects perpendicular to the intertidal shoreline, in the cold season (winter 20...