Abstract
In the field of pattern recognition signals are frequently thought of as the product of sources that act statistically. The goal of the analysis of such signals, therefore, is to model the statistical properties of the assumed signal sources as exactly as possible. As a basis of the model building merely the observed example data and assumptions about limitations in the model’s degrees of freedom are available. However, the model to be determined should not only replicate the generation of certain data as exactly as possible but also deliver useful information for segmenting the signals in some meaningful units.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
5.9 Bibliographical Remarks
Baum L, Petrie T, Soules G, Weiss N (1970) A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Statist 41:164–171
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1):1–22
Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press
Forney GD (1973) The Viterbi algorithm. Proceedings of the IEEE 61(3):268–278
Huang X, Ariki Y, Jack M (1990) Hidden Markov Models for Speech Recognition. No. 7 in Information Technology Series, Edinburgh University Press, Edinburgh pp. 151–152
Huang X, Acero A, Hon HW (2001) Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice Hall, Englewood Cliffs, New Jersey
Jelinek F (1997) Statistical Methods for Speech Recognition. MIT Press, Cambridge, MA
Juang BH, Rabiner LR (1990) The segmental к-means algorithm for estimating parameters of Hidden Markov Models. IEEE Trans on Acoustics, Speech, and Signal Processing 38(9):1639–1641
Lee CH, Rabiner LR, Pieraccini R, Wilpon JG (1990) Acoustic modeling for large vocabulary speech recognition. Computer Speech & Language 4:127–165
Markov AA (1913) ПримБръ статистическаго иэслБдованія надъ текстомъ „Евгенія ОнБгина“ иллюстрирующій свяэъ испЫтаній въ цБпь (Example of statistical investigations of the text of „Eugen Onegin“, wich demonstrates the connection of events in a chain). In: ИэвБстія Императорской Академій Наукъ (Bulletin de l’Académie Impériale des Sciences de St.-Pétersbourg), Sankt-Petersburg, pp 153–162, (in Russian)
Rabiner LR (1989) A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77(2):257–286
Rabiner LR, Juang BH (1993) Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs, New Jersey
Viterbi A (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans on Information Theory 13:260–269
Wellekens CJ (1992) Mixture density estimators in viterbi training. In: Proc. Int. Conf. on Acoustics, Speech, and Signal Processing, vol 1, pp 361–364
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2008). Hidden Markov Models. In: Markov Models for Pattern Recognition. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71770-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-71770-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71766-9
Online ISBN: 978-3-540-71770-6
eBook Packages: Computer ScienceComputer Science (R0)