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To browse Academia. Jorge Baptista. Mark Gales. Luis Javier Rodriguez-Fuentes. Waleed Abdulla. Tuba Qureshi. Natural language processing enables computer and machines to understand and speak human languages.
Speech recognition is a process in which computer understand the human language and processes further instructions as per recognition of the human language. The human language varies so the machine or computer needs entirely different algorithms as the human languages differ in various aspects, such as sounds, phonemes, words, meanings and much more.
Understanding human language is a challenging job and for this purpose Hidden Markov Models are used commonly as they possess promising results in understanding human language. A survey of various researches employing Hidden Markov models is presented to highlight the importance of HMM in the process of speech recognition. Although initially introduced and studied in the late s and early s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years.
There are two strong reasons why this has occurred. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Second the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to carefully and methodically review the theoretical aspects of this type of statistical modeling and show how they have been applied to selected problems in machine recognition of speech.
Renata Vieira. Dafydd Gibbon. Rizwanul Fattah , Vorathin Epin. Eugenia Ortiz Gambetta. Timothy E Carter. Suganya Vijayabaskaran , B. Leidy Daniela. Luke Olbrich. Daneen Jowhill. Simon Dalby. Itamar Freitas. Luciano Micali. Peter Staudenmaier.