This paper examines the impact of anticipated and unanticipated monetary policy announcements, of the Bank of England’s Monetary Policy Committee on UK sectoral stock returns. The monetary policy shock is generated from the change in the three-month sterling LIBOR futures contract. Using a panel GMM estimator we find that both the expected and unexpected components of monetary changes are signi...

Banking sector openness may directly increase growth by improving the quality of financial services and increasing funds available, or indirectly by improving the efficiency of financial intermediaries, both of which may reduce the cost of financing, in turn, increase capital accumulation and economic growth. The objective of the paper is to empirically reinvestigate these direct and indirect l...

Macroeconomic Effects of Short-Term Training Measures on the Matching Process in Western Germany This paper investigates the macroeconomic effects of short term training measures on the matching processes in West Germany. The empirical analysis is based on regional data for local employment office districts for the period from January 2003 to December 2004. The empirical model relies on a dynam...

capital market is one of the most important sectors of every economy. economic growth can lead to capital market boom and development, on the other hand, achieving desired economic growth and development is impossible without efficient financial institution and suitable funding resources. in this study the relationship between financial development and economic growth in opec countries and non-...

Gaussian Mixture Model (GMM) with Fuzzy c-means attempts to classify signals into speech and music. Feature extraction is done before classification. The classification accuracy mainly relays on the strength of the feature extraction techniques. Simple audio features such as Time domain and Frequency domain are adopted. The time domain features are Zero Crossing Rate (ZCR) and Short Time Energy...

In this paper, a class of GMM-based discriminative kernels is proposed for speaker identification. We map an utterance vector into a matrix by finding the sequence of components, which have the maximum likelihood in the GMM for the all frame vectors. And the weights matrix was used, which were got by the GMM's parameters. Then the SVMs are used for classification. A one-versus-rest fashion is u...

In this paper we aim to improve the performance of Gaussian Mixture Model (GMM) classifier using Impostor model parameters for a closed set Speaker Identification task. We propose a novel method of speaker model training which uses the parameters of an Impostor Model to discriminatively train, in order to improve the performance of the GMM based classifier. This is unlike conventional technique...

In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the propo...

To overcome the defects of common used algorithms based on model for abnormal speech recognition, which existed insufficient training data and difficult to fit each type of abnormal characters, an abnormal speech detection method based on GMM-UBM was proposed in this paper. For compensating the defects of methods based on model which difficult to deal with the diversification speech. Firstly, m...

Statistical classifiers operate on features that generally include both useful and useless information. These two types of information are difficult to separate in the feature domain. Recently, a new paradigm based on a Latent Factor Analysis (LFA) proposed a model decomposition into usefull and useless components. This method was successfully applied to speaker and language recognition tasks. ...