A Review on Emotion Recognition Algorithms using Speech Analysis <s> Vector Quantization (VQ) <s> This paper presents a brief survey on Automatic Speech Recognition and discusses the major themes and advances made in the past 60 years of research, so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in this important area of speech communication. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges (e.g., variations of the context, speakers, and environment).The design of Speech Recognition system requires careful attentions to the following issues: Definition of various types of speech classes, speech representation, feature extraction techniques, speech classifiers, database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. Hence authors hope that this work shall be a contribution in the area of speech recognition. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field. <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Vector Quantization (VQ) <s> Language is man's best important means of communication and speech is its main medium. Human Language Technology has gain the important attention owed to the increasing applications in human machine demands Speech Recognition is the capability of a computer to distinguish general, naturally flowing noises from a extensive variety of users. Basically, it resources talking to your computer, and having it properly identifies what you are saying. At the primary level struggle is made to distinguish simple spoken words but the software can further be updated and improved in order to cover extra operations. <s> BIB002
Vector quantization (VQ) is a process of mapping feature vectors of test utterance to the best matching feature vectors of the reference models BIB002 . As compared to other techniques such as HMM, VQ boosts is its low computational burden due to its straightforward approach. The efficiency is due to its nature of using compact codebooks for reference models and codebook searcher BIB001 . While the basic VQ appears to be convenient, because the vectors are jumbled up, VQ does not take into account the temporal evolution of the signals.