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The fuzzy HMM algorithm is regarded as an application of the fuzzy expectation-maximization (EM) algorithm to the Baum-Welch algorithm in the HMM. The Texas Instruments p4 used speech and speaker recognition experiments and show better results for fuzzy HMMs compared with conventional HMMs. Equation and how estimation of discrete and continuous HMM parameters on based this two algorithm is explained and performance of two speech recognition method for one hundred is surveyed. This paper show better results for the fuzzy HMM, compared with the conventional HMM. After of that work we use Fuzzy-Neural Network system was proposed for Farsi speech recognition. Instead of using the fuzzy membership input with class membership desired-output during training procedure as proposed by several researches. we used the fuzzy membership input with fundamental binary desiredoutput. This can reduce the misunderstood training, decrease the training time and also improve the recognition ability.

The fuzzy HMM algorithm is regarded as an application of the fuzzy expectation-maximization (EM) algorithm to the Baum-Welch algorithm in the HMM. The Texas Instruments p4 used speech and speaker recognition experiments and show better results for fuzzy HMMs compared with conventional HMMs. Equation and how estimation of discrete and continuous HMMparameters on based this two algorithm is explained and performance of two speech recognition method for one hundred is surveyed. This paper show better results for thefuzzy HMM, compared with the conventional HMM.A fuzzy clustering based modification distances in the FCM functionals are redefined as the negative of logarithms of density functions, which are of Gaussian products of mixture weights and Gaussian functions. mixture models (GMMs) for speaker recognition is proposed. In this modification, fuzzy mixture weights are introduced by redefining the distances used in the fuzzy c-means (FCM) functionals. Their re estimation formulas are proved by minimizing the FCMfunctionals.

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This paper proposes a Ftuzzi approach to the Hidden Markov Model (HMM). This method called the fizzy HMMfbr speech and speaker recognition as an application offizzy expectation maximizing algorithm in HMM. The fitzzy HMM algorithm is regar-ded as an application of' the ,fizzy expectation-maximization (EM) algorithm to the Batum-Welch algorithm in the HMM. The Texas Instruments p4 uised speech and speaker recognition experiments and show better results fbr.fiuzzv HMMs compared with conventionalHMMs. Equation and how estimation of discrete and continuious HMM parameters on based this two algorithm is explained and perfbrmance of two speech recognition method tbr one hundred is surveved. This paper show better results for the fiuzzy HMVM, compared with the conventional HMM.