Analysis of speech recognition techniques

This paper focuses on speech recognition techniques such as LPC(linear predictive coding), MFCC(Mel-frequency Cepstral coefficients) with Hidden Markov Models, LPCC(linear predictive Cepstral coding), and RASTA and will compare these techniques to find a most accurate and efficient way to recognize speech. Speech recognition is the process in which program or machine do the identification of words or phrases and convert them to machine-readable format. Additionally, this paper also focuses on NLP(natural language processing) techniques used with the speech recognition process. Once the speech signal is converted to text then NLP is used to understand and generate what has been said. NLU(natural language understanding) and NLG(natural language generation) are two important steps in NLP, through this paper, we will compare and analysis techniques to find out which we can use with speech recognition for effective results. The Objective of this paper is to find out the best technique which is currently used.