Unalike Methodologies of Feature Extraction & Feature Matching in Speech Recognition

In this present scenario, the application of speech science has a vital role to produce the biometric applications. After so many research and improvement of Automatic Speech Recognition, accuracy of speech recognition is one of the challenging task. Various feature extraction techniques like Linear Predictive Coding, cepstral analysis, Local Discriminant Base, Restricted Boltzmann Machines have been discussed since past days. Similarly, a lot of debates have been arranged among the researchers for feature matching. Some of them are Hidden Markov Model (HMM), Dynamic time warping (DTW), Deep Belief Network. This paper is a clear reflection of automatic speech recognition. It describes various feature extraction and matching and focuses on analytical study based on performance metrics like Word Error Rate (WER) and accuracy of these techniques.