Spoken Keyword Based Speech Recognizer for Extracting Information

Abstract: This paper investigates multilayer feed forward back-propagation artificial neural network hybridized with ontology, a knowledge engineering tool for developing speech recognizer that will assist to retrieve Indian tourism information in hands free mode. Spoken key words are collected from 30 speakers of both genders in Indian tourism domain. This paper also used ontology technique a knowledge engineering approach in order to reduce the search space during information extraction. Thirteen Mel frequency cepstral coefficients have been applied to represent speech signal. Features from the selected frames are used to train multilayer FFBPNN. The same routine is applied to the speech signal during the recognition stage in such a way that unknown test patterns are classified to the nearest patterns and retrieved. The system can be said to be robust as average accuracy for clean data is 88.6 % % while that for noisy data is 67.7 %