Reduction of time for speech recognition system by shrinking adaptive layers of ADAG for real time system using ADAT SVM

Abstract

Recently speech recognition becomes very major area for large vocabulary real time applications. In the existing work research was formulated for large number of words spoken by same speaker using Adaptive Directed Acyclic Graph (ADAG) with support vector machines. In today’s era the emphasis is given to processing large vocabulary data process and recognition. In existing system, when spoken words are recognizing by number of adaptive layers causes increase in testing time for recognition of data occur. The aim of the proposed Reduced Adaptive Directed Acyclic technique (RADAT) is to develop a system to recognize test word in less amount of time than existing System [1]. In our current system it is possible to remove unnecessary finding distance of test word with number of times with training word. The proposed RADAT system handles this by applying reduction in number of adaptive layers to recognize any testing word and which result reduction in time. The experiment results of current system reduce time complexity without loss of recognition accuracy for any data of speaker dependent system. The results are obtained for various dataset like large vocabulary speech record as well for small size data to large vocabulary dataset. In our paper we use same feature extracted samples for training and testing. This method is work for various speech dataset like machine operation command in English or required language to operate computer system in real time application as well applied for offline application such as start, logoff, login, notepad, open, close, shutdown etc [5].