Abstract: Speech Recognition is the technology by which sounds, words or phrases spoken by humans are converted into electrical signals and these signals are transformed into coding patterns to which meanings are assigned. It has two main types: discrete word and continuous speech recognition systems. Each type can be further sub-divided into two categories as Speaker Dependent and Speaker Independent recognition systems. Speaker dependent system operates only on the speech of a particular speaker for which the system is trained, while the Speaker Independent systems can be operated on the speech of any speaker. The speech recognition system proposed here digitizes the isolated words spoken by a speaker and performs Mel Frequency ceptral analysis and other signal processing techniques on the digitized data. The processed speech signal is then passed on to a pattern recognition which takes action based on the type of command pattern received. Artificial Neural Network (ANN) is used as speech recognition engine. Two different corpora were collected of audio recordings of Yoruba, Igbo and Hausa language speakers, in which subjects read aloud different words. One of the collected corpora contained data with background noise and the other without background noise. The results obtained from simulation can be generalized to cater for larger vocabularies and for continuous speech recognition.

Abstract: Grid Computing is a computing framework developed to meet the growing computational demands. Essential grid services contain more intelligent functions for resource management, grid service marketing, collaboration etc. The load sharing of computational jobs is the major task of computational grids. Grid resource manager provides functional mechanism for discovery, publishing of resources as well as scheduling, submission and monitoring of jobs. This paper introduces an approach, based on Differential Evolution Algorithm for scheduling jobs on computational grid. The proposed approach generates an optimal schedule which helps in completing the jobs within a minimum period of time. We evaluate the performance of our proposed approach with a direct Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) approach.

Abstract: National Telecommunication Regulator in many countries around the world imposes call termination taxes on national calls as well as international calls landing in that country. In many third world countries, every year up to 90 % of the international traffic bypasses regulatory checks, causing a huge revenue loss to the country. The use of illegal gateways to bypass the Voice Clearing Houses to terminate international traffic using VoIP gateways, GSM / local line branch exchanges or other related equipment are the simplest means of Grey traffic. Use of the encryption and other network design techniques are the easiest methods to hide the VoIP traffic from any clearing house. We have proposed an architecture based on a mathematical model to detect, segregate and qualify VoIP traffic (Grey) into different categories. The proposed model identifies grey traffic, through traffic analysis techniques coupled with statistical anomaly based intrusion detection system and behavior analysis.