The Ideal Usage of Sustainable Materials and Local Resources of the Interior Space Design in Jordan

Buildings generate a large amount of waste throughout their life cycles, from construction and building operations to demolition. The amount of waste leaving the properly can be reduced, however, through responsible procurement choices, as well as by implementing comprehensive recycling programs throughout the construction, operation, and demolition phases. Consideration for materials and resources focuses on the health and productivity consequences of material selections for building occupants, plus the long term social, economic, and environmental impacts of materials used in the design and construction of the building. Green building addresses two kinds of problems related to materials and resources： waste management and life-cycle impacts. This issue has been discussed by many professionals and researchers and it seems this problem is more likely existed in not developing countries comparing with developing countries. The lack of selecting the right materials, have not been well taken into the consideration. Researchers have developed a number of assumptions that helps to resolve the research problems, which includes the application of the green material and resource in the Jordauian interior designs to provide a healthy environment to the interior spaces. Therefore, the paper aims to search for the possibilities of proposing some indicators using sustainable material and resource in the of internal Jordanian spaces. The theoretical part goes through a brief study to definition of sustainable material and resource in Jordan, and its uses in all the elements of internal and emphasis about the traditional symbols to preserve the identity of Jordan, then we going through the uses of the material and resources by analyses examples of the green interior spaces in Jordan.

Research on Tibetan Speech Recognition Based on the Am-do Dialect

In China,Tibetan is usually divided into three major dialects:the Am-do,Khams and Lhasa dialects.The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan.Although this dialect has its own specific historical and social conditions and development,there have been different degrees of communication with other ethnic groups,but all the abovementioned dialects developed from the same language:Tibetan.This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language,which optimizes the problems existing in previous research.Audio data of the Am-do dialect are expanded by data augmentation technology combining noise and reverberation,and the morphological characteristics and characteristics of the Tibetan language are further considered.According to the particularity of Tibetan grammar,grammatical features are used to optimize grammatical relationships and are combined with a language model,and the Am-do dialect is scored and rescored.Experimental results show that compared with the baseline,our proposed new lexicon and data augmentation technology yields a relative increase of approximately 3%in character error rates(CERs)and a relative increase of 3%-19%in the recognition rate of acoustic models and language models.

An iterative approach to Bayes risk decoding and system combination

We describe a novel approach to Bayes risk(BR) decoding for speech recognition,in which we attempt to find the hypothesis that minimizes an estimate of the BR with regard to the minimum word error(MWE) metric.To achieve this,we propose improved forward and backward algorithms on the lattices and the whole procedure is optimized recursively.The remarkable characteristics of the proposed approach are that the optimization procedure is expectation-maximization(EM) like and the formation of the updated result is similar to that obtained with the confusion network(CN) decoding method.Experimental results indicated that the proposed method leads to an error reduction for both lattice rescoring and lattice-based system combinations,compared with CN decoding,confusion network combination(CNC),and ROVER methods.

Investigation of Improved Approaches to Bayes Risk Decoding

Bayes risk (BR) decoding methods have been widely investigated in the speech recognition area due to its flexibility and complexity compared with the maximum a posteriori (MAP) method regarding to minimum word error (MWE) optimization. This paper investigates two improved approaches to the BR decoding, aiming at minimizing word error. The novelty of the proposed methods is shown in the explicit optimization of the objective function, the value of which is calculated by an improved forward algorithm on the lattice. However, the result of the first method is obtained by an expectation maximization (EM) like iteration, while the result of the second one is achieved by traversing the confusion network (CN), both of which lead to an optimized objective function value with distinct approaches. Experimental results indicate that the proposed methods result in an error reduction for lattice rescoring, compared with the traditional CN method for lattice rescoring.