Gaussian Mixture Model (GMM) is a simple and effective method for statistical modeling of the feature space which is widely used in spoken language recognition systems and EM algorithm is used for training the parameters of this model. In this paper, considering the weakness of GMM models, a new model named PAW-GMM is proposed. In this model, the power of each component of GMM in discriminating one language from the others is considered for determining the weights of components. Since PAW-GMM considers the discriminating property of GMM components, it could increase the accuracy of language recognition systems. Also one of the problems of GMM-PSK-SVM which is one of the best GMM models is the high complexity especially for high number of languages. Therefore UBM-PSK-SVM is proposed that has the same accuracy as GMM-PSK-SVM but lower complexity. Experiments on four languages of OGI corpus show the efficiency of the proposed techniques.

To explain the estimation of conservatism as a limiting covenant in accounting, the current paper discussed Basu (1997) conservatism model and Nichols (2010) generalized conservatism model and examined the problems about these models as regards the validity and generalizability of the results of the research on this topic .Accordingly, the discussed problems were explained and the solutions for solving these problems and upgrading the model to estimate conservatism were presented. The data from 2006 to 2016 on 87 corporations were used to carry out the investigations. Also, in addition to the static routine estimator, the dynamic panel estimator (the GMM-sys method) was used to test and estimate the models. The current research results revealed that the new modified conservatism estimation model yielded more appropriate results than the original Basu model and Nichols generalized model. Moreover, it indicated that the dynamic method of model estimation is more suitable than the static one. Finally, the current research showed that the results of the previous research conducted using Basu model to measure conservatism should be considered more cautiously and their application in the capital markets calls for re-examination.

In a voice conversion system speech signal of A speaker (i.e. source speaker) is modified so that it sounds as if it had been pronounced by B speaker (i.e. target speaker). This process, sometimes, is called speaker conversion (changing speaker identity). Achieved signal from speaker conversion system is desired to have high quality and very natural. To satisfy this, three major methods are proposed as follows: VQ_based, LMR_based and GMM_based voice conversion methods. DTW is the most popular way to warp corresponded words in two sentences. In this paper, DTW is used to design corresponding transfer function. To decrease the distance between two speakers, DTW warps the couple phonemes of two speakers, instead of two words or couple sentences while a linear temporal transform which depends on phonemes is used for error decreasing. By using other appropriate corrections that are used in learning and designing of the linear transforms, a high quality voice conversion system is achieved.    

In spite of a lot of studies in field of housing, housing supply has been less addressed. One of main reasons is complexity of housing supply field. In this study we use Capozza and Helsley urban growth model which assume housing supply as a function of variations in both housing price and construction expenses. The model is estimated with General Method of Moments (GMM) using seasonal data of Iran for 1996:2-2008: I. Results show that effect of housing price variations on housing supply is positive, however the effect of building material expenses and construction opportunity cost (stock exchange index) are both negative on it, but coefficient of stock exchange index is statistically insignificant. In addition, housing price variations and building material expenses with two period lags are effective on housing supply which indicate role of expectations. Also results show that variations in the other markets such as building materials market play important role in housing supply. It is proposed that because of high sensitivity in increasing of housing price, policies of housing supply to be followed by this market.

Management earnings forecast is one of the most important information resources in capital markets. The literature suggests managerial overconfidence is an effecting factors on the earnings forecasts’ accuracy. Because of users' relying to forecasted information, examination of the bias's effects on forecasts' accuracy is important. By using a new managerial overconfidence assessment model and Generalized Method of Moments (GMM) regression analysis, the paper examines this managerial bias’ s effect on management earnings forecasts’ error in the firms that have listed at Tehran Securities Exchange (TSE) during the period from year 2007 to year 2016. The results show Chief Executive Officers’ overconfidence has a significant positive effect on earnings forecasts’ error, how overconfident Chief Executive Officers overestimate earnings forecasted above than actual earnings. This finding is consistent with recent researches’ ones and suggests information's users should be aware of Chief Executive Officers' overconfidence's negative effects on reliability of managerial forecasted information.

Speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. A speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. When a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect on speech signal and also identity falsification with speech synthesis. In this system, we have proposed a new speaker verification system based on Multi Model GMM, called SV-MMGMM, in which all speakers are divided into seven different age groups, and then an isolated GMM model for each group is created; instead of one model for all speakers. In order to evaluate, the proposed method has been compared with several speaker verification systems based on Naïve, SVM, Random Forest, Ensemble and basic GMM. Experimental results show that the proposed method has so better efficiency than others.

GMM is one of the most successful models in the field of automatic language identification. In this paper we have proposed a new model named adapted weight GMM (AW-GMM). This model is similar to GMM but the weights are determined using GMM-VSM LID system based on the power of each component in discriminating one language from the others. Also considering the computational complexity of GMM-VSM, we have proposed a technique for constructing bigram sequences of components which could be used for higher sequence orders and decreases the complexity. Experiments on four languages of OGI corpus including English, Farsi, French and German have shown the effectiveness of proposed techniques.

The research method in this study is applied based on the purpose of the research is practical type and based on correlation method. The statistical population includes all managers and experts of the Telecommunication Infrastructure Company in the number of 1590 people which the number of samples was considered 310 people using Cochran's method. The first questionnaire was compilated and distributed for 51 identified factors and after Delphi analysis it was decreased to 18 factors. Then, a secondary questionnaire with 63 questions was compilated with the help of extracted factors and with the help of confirmatory factor analysis, the authenticity of the questions was confirmed. Finally, the final model was tested using confirmatory factor analysis and AMOS software. All components identified in the statistical test were confirmed.