In this paper we propose a Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Hierarchical Clustering. Each speaker speech spectra are characterized with a mixture of Generalized Gaussian Distribution includes Gaussian and Laplacian distribution as a particular case. It also includes several of the platy, lepto and meso kurtic shapes of the speech spectra. The speech analysis is done with Mel Frequency Cepstral Coefficients extracted from front end process. Using the EM algorithm the model parameters are estimated. The numbers of acoustic classes associated with each speech spectra are determined through Hierarchical clustering. The performance of the proposed algorithm is studied through experimental evolution with 100 speaker"s data base and found that this algorithm outperforms the existing speaker identification algorithm with GMM. It is also observed that this algorithm performs efficiently even heterogeneous population with small (less than 2 seconds utterances)
A novel approach for energy-aware management of sensor networks that maximizes the life of the sensors while maintaining desired quality of service attributes related to sensed data delivery is presented. This approach is to dynamically set routes and arbitrate medium access to minimize energy consumption and maximize sensor life. It presents a brief overview of the dynamic source routing and describes a TDMA based medium access control protocol (MAC).Hence the proposed APCRP-MAC
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