The development of pattern recognition methods on the basis of so-called Markov
models is tightly coupled to the technological progress in the field of automatic
speech recognition. Today, however, Markov chain and hidden Markov models are
also applied in many other fields where the task is the modeling and analysis of
chronologically organized data, for example genetic sequences or handwritten texts.
Nevertheless, in monographs, Markov models are almost exclusively treated in the
context of automatic speech recognition and not as a general, widely applicable tool
of statistical pattern recognition.
In contrast, this book puts the formalism of Markov chain and hidden Markov
models at the center of its considerations. With the example of the three main application areas of this technology — namely automatic speech recognition, handwriting
recognition, and the analysis of genetic sequences — this book demonstrates which
adjustments to the respective application area are necessary and how these are realized in current pattern recognition systems. Besides the treatment of the theoretical
foundations of the modeling, this book puts special emphasis on the presentation
of algorithmic solutions, which are indispensable for the successful practical application of Markov model technology. Therefore, it addresses researchers and practitioners from the field of pattern recognition as well as graduate students with an
appropriate major field of study, who want to devote themselves to speech or handwriting recognition, bioinformatics, or related problems and want to gain a deeper
understanding of the application of statistical methods in these areas