LAUSR

Abstract Digital audio spoken vocabulary retrieval and recognition have been developed in recent years. This growth has prompted extensive research into technology and reliable indexing. The English vocabulary content retrieval requires a combination of audio and speech processing technology and information retrieval. A study of English content search initially investigated units of planning to have a similar audio signal structure, and the subsequent focus outside the audio, naturally, more informal oral content. Shifted to generate conversational settings and the volume provided by this study outlines the component technical discipline of the relationship between lexical speech signal recognition and user interaction issues. The aim is for researchers with a background in speech technology to seek a deeper understanding of whether these fields are integrated into supporting research and development to solve the core challenges of vocabulary and content search. This study describes a content-based search method for Machine Learning based Neural Network classifiers that retrieve relevant documents efficiently and accurately. This overcomes the system limitations of characterization based arrangements regarding restricted vocabulary and preparing information accessibility. To analysis the system, it has designed a one-time learning neural network for classification and achieves better accuracy results.