The Development of a Speech Recognition Software Application Using HMM, ANN and LPC Models to Minimize Error

Abstract

The purpose of this application is to develop speech recognition software applications and test it. The developed speech recognition system for human language solves for processing the voice input and answering the queries of the user. This system begins with an introduction to Speech Recognition Technology then it explains how it works, and the level of accuracy that can be expected. The system is considered for the possible applications of speech technology, including every single task related with the action of the human voice. In this sense, the application fields can vary from speech production, storage, transmission and recognition processes. This research pertains to discuss the theories and models on speech communication and more importantly the use of technology for speech recognition. Several authors’ views are discussed but a great emphasis is given to the Hidden Markov Models (HMM), Artificial Neural Network (ANN) and Liner Predictive Coding (LPC). The aim of the research is to test these models and outline voice recognition algorithms which are important in improving the voice recognition performance. The application is tested with speakers used to represent different tones, emotions and lengths in metres as a sample of relationships methodology, the reports the system permits are used in the results analysis.