Classification Techniques used in Speech Recognition Applications: A Review

Classification phase is one of the most active research and application areas of speech recognition. The literature is vast and growing. This paper summarizes the some of the most important developments in the classification procedures of the speech recognition applications. The state of art of the classification technique has also been presented in this paper. Different classification techniques and their parameter estimation methods, properties, advantages, disadvantages along with their application areas are discussed with each classification method. Our purpose is to provide a synthesis of the published research in the area of speech recognition and stimulate further research interests and efforts in the identified topics. This paper presents an overview of several pattern classification methods available in literature for speech recognition applications. Keywords— Classification, Classifiers, Taxonomy, Bayes decision theory, Acoustic Phonetic approach, Template matching, Dynamic Time Warping(DTW),Vector Quantization(VQ), Hidden Markov Model(HMM), Artificial Neural Network(ANN), Support Vector Machine(SVM), KNearest Neighbor(KNN), Gaussian Mixture Modeling, Clustering techniques, Evaluations, Applications.