Informatics Publishing Limited

R. Rajeswara Rao ¹, A. Prasad , Ch. Kedari Rao ²

Affiliations
1 Department of Computer Science & Engineering, MGIT, Hyderabad, IN
2 Department of CSE, DVR College of Engineering & Technology, Hyderabad, IN

Abstract

In this paper, robust feature for text-independent speaker recognition has been explored. Through different experimental studies, it is demonstrated that, these robust features captures speaker specific information effectively by using Ergodic hidden Markov models (HMMs). The study on the effect of feature vector size for good speaker recognition demonstrates that, feature vector size in the range of 18-22 can capture speaker specific related information effectively for a speech signal sampled at 16 kHz, it is established that the proposed speaker recognition system using robust features requires significantly less amount of training data during both in the training as well as in testing. Finally, the speaker recognition studies using robust features for different mixtures components, training and test durations have been exploited. We demonstrate the speaker recognition studies on TIMIT database.

Keywords

Hidden Markov Models (HMMs), MFCC, Robust Features and Speaker.

Full Text

     

Yadaiah Balagoni ¹, R. Rajeswara Rao ²

Affiliations
1 Department of Computer Science and Engineering, MGIT, Gandipet Main Road, Kokapet, Hyderabad - 500075, Telangana, IN
2 Department of Computer Science and Engineering, JNTU Vizianagaram, Dwarapudi, Vizianagaram - 535003, Andhra Pradesh, IN

Abstract

Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms.

Keywords

Cloud Environment, Heterogeneous Cloud, Locality-Load-Prediction Aware Scheduling, Multi-Objective, Task Scheduling

Full Text