This paper presents a novel technique that allows testing spoken dialogue systems by means of an automatic generation of conversations. The technique permits to easily test spoken dialogue systems under a variety of lab-simulated conditions, as it is easy to vary or change the utterance corpus used to check the performance of the system. The technique is based on the use of a module called user simulator whose purpose is to behave as real users when they interact with dialogue systems. The behaviour of the simulator is decided by means of diverse scenarios that represent the goals of the users. The simulator aim is to achieve the goals set in the scenarios during the interaction with the dialogue system. We have applied the technique to test a dialogue system developed in our lab. The test has been carried out considering different levels of white and babble noise as well as a VTS noise compensation technique. The results prove that the dialogue system performance is worse under the babble noise conditions. The VTS technique has been effective when dealing with noisy utterances and has lead to better experimental results, particularly for the white noise. The technique has permitted to detect problems in the dialogue strategies employed to handle confirmation turns and recognition errors, suggesting that these strategies must be improved. q 2002 Elsevier Science B.V. All rights reserved.

1.4 The norm’s accepted ASTM Asm ANSI Cei Isa IEEE Nfpa Cee Oms En,afnor,bs Asme 1.2 technique condition Capacity 2 m3 /h for 8h non stop ???? To be confirmed. The system most be plugged to the national water network (Ade) at the pressure of 3 bars And temp following the ambient condition The caracteristique of the water to be treated is noted in the chapter 2.2.1 General demand Reference in this field. Experience in this type of installation Experience in the treatment of water reference in Algeria (all type of project) 3.1 The technique of water treatment Ref: drawing .1. Done by Didine Abdoune following 3.1 3.3 demands specified 1 traitement of water most following this point The treatment of water most be done following the data in the table of the caracteristique of the water table 1 Treatment of water containing floating particle Treatment of water wear the turbidity is high Correction of the smell the test and elimination of the chloral And the water most not contain um1 Treatment of bacterologique and disinfection and sterilization of the water For the sterilization we most use the UV method ///// most is in the norm of OMS OR THE EUROPEN UNION Demand of the equipments, most be \\\\\ Most be storage tank of the capacity of 15 M3 for the no treated water (venting valve) and man hole, over flow line, bady switched to indicate the level for the pump number 2 So the start and stop following the info given from the BDY SWICHE Storage tank for produced water in capacity of 4m3 x 2 in parallel (valve for by passe) sit glass safety valve and over flow, man hole. Bd swich for the control of start and stop of pump n 2 Pipeline Material most be STM a 120 SCH 80 OR EQUIVALENT The connection from the man most be EPOER PIPE MOST STAND THE FOLOWING SITUATION - dilation and the contraction thermique - vibration - effects of the temperature - support flexibility - over thickness 1,6 mm for the corrosion - the sludge and drain most be connected to the existing system - the treed most be type NPT ,drilled and treed conform to ANSI B 1.20.1 - The flanges most be ANSI class 150/PN20 - Manometer most be installed in the piping - No return valve drainer , venting , and sample - The pvc and the aplomb are excluded from the installation Civil engineering and still structure - All unites most be mounted in skids and chassis the chassis will be fix in foundation plate form of beton armed and welded inside structure the chassis most be from galvanized type and will be having low ding point - The offers most have also the offer of the installation housing still structure and...etc all proposition are well come the cover of the housing most be maid of TN40 - Construction most be erthequek proven Instrumentation and control - The installation most have the measurement and control of the following - The hardness - The ph - Pressure indicator - Pressure in the filters and alarms Power valuable - 380 v - phase 03 neutral - frequency HZ 50 Spare parts - list of spare parts for period of 2 years this list most have consumable part and changing part for the total unite - Most be list of spare parts of start up. - including the tousle and special tousles for the operation and maintenance of the installation - if there is chimiquell product to included in the prosses most provide the information of this one The noise restriction is 85 db The provider most guaranty 20 years of providing ( ) 20 years of spar parts The time of delivery is 6 month staring the day of the signature of the contract? Test the unite most be tested in eyes whiteness of 2 person from the sonatrach The submission most include - drawings of the implantation of the unite - foundation drawing and the still structure - process drawings - electrical drawings - specifications of the equipment List data scheet of the instrumentation Isometriques drawings Operating manuals Planning for the global phases

We implement variable n-grams using a word-tree data structure, where nodes represent sequences of words ("contexts") and store how often that context appeared in a training corpus. We build the tree by growing it from the root up. Unlike other methods, there is no pruning step. Instead, we used the simple heuristic of maintaining a priority-queue of candidate leaves, sorted by how often those contexts occurred in the training text. The most popular leaves are then added to the tree, and this process repeats until a specified memory limit is reached. In this way, the tree was able to make branches for longer sentence fragments like "across the street from the" while saving the space from storing uncommon ones.

Statistical language models should improve as the size of the n-grams increases from 3 to 5 or higher. However, the number of parameters and calculations, and the storage requirement increase very rapidly if we attempt to store all possible combinations of n-grams. To avoid these problems, the reduced n-grams ’ approach previously developed by O’Boyle (1993) can be applied. A reduced n-gram language model can store an entire corpus’s phrase-history length within feasible storage limits. Another theoretical

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We experiment with splitting words into their stem and suffix components for modeling morphologically rich languages. We show that using a morphological analyzer and disambiguator results in a significant perplexity reduction in Turkish. We present flexible n-gram models, Flex-Grams, which assume that the n−1 tokens that determine the probability of a given token can be chosen anywhere in the sentence rather than the preceding n − 1 positions. Our final model achieves 27 % perplexity reduction compared to the standard n-gram model. 1

Language model plays an important role in statistical machine translation systems. It is the key knowledge source to determine the right word order of the translation. Standard n-gram based language model predicts the next word based on the n − 1 immediate left context. Increasing the order of n and the size of the training data improves the performance of the LM as shown by the suffix array language model and distributed language model systems. However, such improvements narrow down very fast after n reaches 6. To improve the n-gram language model, we also developed dynamic n-gram language model adaptation and discriminative language model to tackle issues with the standard n-gram language models and observed improvements in the translation qualities. The fact is that human beings do not reuse long n-grams to create new sentences. Rather, we reuse the structure (grammar) and replace constituents to construct new sentences. Structured language model tries to model the structural information in natural language, especially the long-distance dependencies in a probabilistic framework. However, exploring and using structural information is computationally expensive, as the number of possible structures for a sentence is very large even with the constraint of a grammar. It is difficult to apply parsers on data that is different from the training data of the treebank and parsers are usually hard to scale up. In this