Abstract. Automatic recognition of spoken alphabets is one of the difficult tasks in the field of computer speech recognition. In this research, spoken Arabic alphabets are investigated from the speech recognition problem point of view. The system is designed to recognize spelling of an isolated word. The Hidden Markov Model Toolkit (HTK) is used to implement the isolated word recognizer with phoneme based HMM models. In the training and testing phase of this system, isolated alphabets data sets are taken from the telephony Arabic speech corpus, SAAVB. This standard corpus was developed by KACST and it is classified as a noisy speech database. A hidden Markov model based speech recognition system was designed and tested with automatic Arabic alphabets recognition. Four different experiments were conducted on these subsets, the first three trained and tested by using each individual subset, the fourth one conducted on these three subsets collectively. The recognition system achieved 64.06% overall correct alphabets recognition using mixed training and testing subsets collectively.
A headline is considered a condensed summary of a document. The necessity for automatic headline generation has been on the rise due to the need to handle a huge number of documents, which is a tedious and time-consuming process. Instead of reading every document, the headline can be used to decide which ones contain important and relevant information. There are two major approaches to automatic headline generation. The first is linguistic, in which the knowledge about the structure of the language itself is considered. The second approach is statistical and it comprises all quantitative approaches to automated language processing. However, the Arabic language has a different statistical structure than the English language, and requires special treatment to generate Arabic headlines, especially when there is no dedicated technique for the Arabic language. Therefore, two new statistical methods in automatic headline generation have been developed to create representative headlines for textual documents in the Arabic language. The first is an extractive method based on character crosscorrelation, and the second one is an abstractive method based on the hidden Markov model (HMM). The extractive method achieved ROUGE-L of (0.1938) and the HMM method achieved ROUGE-L of (0.2332). In addition, both techniques were assessed via human examiners who evaluated the resulting headlines.
Chemical threats in open war fields or terrorist attacks are a serious possibility. Chemical leakage, mass destruction weapons and terrorism attacks are some sources of exposure to chemical agents. If a treatment procedure is implemented soon enough to patients exposed to chemical agents, the number of victims will certainly be reduced. Therefore, the need of an available expert who can diagnose the chemical agent and provides the proper treatment is of a paramount necessity. However, there is a lack for such experts. In this paper we introduce a rule-based expert system that diagnoses the patients and provides a decision mechanism to determine which chemical agent the patient has been exposed to. According to our knowledge, no expert system has been developed for this problem. We will demonstrate the characteristics and side effects of different types of chemical agents and we will choose the important and appropriate set of signs and side effects that construct our decision tree in order to build our knowledge base and inference rules. The CLIPS expert system is used to develop the knowledge-based system. We believe that the proposed expert system would provide a good assistant to medical teams especially in critical periods of time.
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