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Information and Computer Science Department Research Profile. Dr. Wasfi Al-Khatib Information and Computer Science Department King Fahd University of Petroleum & Minerals. Information and Computer Science Faculty. 25 Professorial Rank faculty members 1 Full Professor 5 Associate Professors
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Dr. Wasfi Al-Khatib
Information and Computer Science Department
King Fahd University of Petroleum & Minerals
Two types of speech units were used independently: The first consists of 375 diphones of Arabic sounds, and the other has 178 allophones which cover Arabic and English sounds. The project developed extensive Arabic linguistic tools including: Arabic pronunciation rules, and tables of irregularly pronounced Arabic words, and allophone/diphone selection rules. A parametric model was also built to synthesize the speech and to give the user control over the pitch rate, stress, and speech tempo.
We formulated the problem of generating Arabic diacritized text from unvoweled text using Hidden Markov Models (HMM) approach. The word sequence of unvoweled Arabic text is considered an observation sequence from an HMM, where the hidden states are the possible diacritized expressions of the words. The optimal sequence of diacritized words (or states) are then obtained efficiently using a Viterbi like Algorithm. The first phase of this project has already achieved 94.5% letter accuracy.
The proposed project aims at investigating various structures for ANN/HMM models for phoneme recognition or next generation Arabic Speech recognition. Carnegie Mellon Sphinx-4 will be used as our testing platform.
using Fuzzy C-Means Clustering
FUNCTIONAL NETWORKS AS A NEW
FRAMEWORK FOR PATTERN RECOGNITION
Functional networksare a generalization of neural networks. They are capeable of capturing& representing complex input/output relationships.
We assume that the probability can be written as:
where are unknown, but unrestricted functions to be learned from the data, and p(.) must satisfy the two probability conditions, and is unknowns. For example, p(.) can be a Probit or Sigmoidal or CDF or Mulinomial logistic functions.
In functional networks, we learn functions (not parameters)
by approximate them by linearly independent family:
The parameters can be learned using optimization methods.
The response is:
We useConstrained Least Squares, or Iterative Least Squares, or Maximum Likelihood
The real Databases under study are taken from:
Machine learning repository database at UC Irvine:
The normal distribution is given by:
P(x) is the probability density that an observation x is measured in the data-set described by a mean m and standard deviation s.
In statistics the area under the curve described by the normal distribution represents the probability of a variable x falling into a range, say between x1 and x2.
The curve itself represents the relative probability of variable x occurring in the distribution. That is to say, the mean value is more likely to occur than values 1 or 2 standard deviations from it.
This curve is used to estimate the relative probability or “fuzzy possibility” that a data value belongs to a particular data set. If a litho-facies type has a porosity distribution with a mean m and standard deviation s the fuzzy possibility that a well log porosity value x is measured in this litho-facies type can be estimated using Equation 1. The mean and standard deviation are simply derived from the calibrating or conditioning data set, usually core data.
Service Mining Agents represent the information or services presented by the URLs of the Web portal.
SMA nAgentfying the E-Commerce Web Portals
Natural Language InterfaceThe AgenTV