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- Amari, 1967
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Amari, S. (1967).
A theory of adaptive pattern classifiers.
IEEE Transactions on Electronic Computers, 16(3):299-307.
- Bahl et al., 1986
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Bahl, L., Brown, P., de Souza, P., and Mercer, R. (1986).
Maximum mutual information estimation of hidden Markov model
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In Proceedings of the IEEE International Conference on
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Bahl, L., Brown, P., de Souza, P., and Mercer, R. (1988).
A new algorithm for the estimation of hidden Markov model
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In Proceedings of the IEEE International Conference on
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- Baker, 1975
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Baker, J. M. (1975).
The DRAGON system - an overview.
IEEE Transactions on Acoustics, Speech, and Signal Processing,
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Baldi, P. and Chauvin, Y. (1996).
Hybrid modeling, HMM/NN architectures, and protein applications.
Neural Computation, 8:1541-1561.
- Bauer et al., 1996
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Bauer, H.-U., Der, R., and Herrmann, M. (1996).
Controlling the magnification factor of self-organizing feature maps.
Neural Computation, 8(4):757-771.
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Baum, L. (1972).
An inequality and associated maximization technique in statistical
estimation of probabilistic functions of Markov processes.
Inequalities, 3:1-8.
- Baum and Petrie, 1966
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Baum, L. and Petrie, T. (1966).
Statistical inference for probabilistic functions of finite state
Markov chains.
Annals of Mathematical Statistics, 37:1554-1563.
- Bellegarda and Nahamoo, 1990
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Bellegarda, J. and Nahamoo, D. (1990).
Tied mixture continuous parameter modeling for speech recognition.
IEEE Transactions on Acoustics, Speech, and Signal Processing,
38(12):2033-2045.
- Bocchieri, 1993
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Bocchieri, E. (1993).
Vector quantization for the efficient computation of continuous
density likelihoods.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 2, pages 692-695.
- Bourlard, 1995
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Bourlard, H. (1995).
Towards increasing speech recognition error rates.
In Proceedings of 4th European Conference on Speech
Communication and Technology, pages 883-894, Madrid, Spain.
- Bourlard and Wellekens, 1990
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Bourlard, H. and Wellekens, C. J. (1990).
Links between Markov models and multilayer perceptrons.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
12(12):1167-1178.
- Bradburn, 1989
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Bradburn, D. (1989).
Reducing transmission error effects using a self-organizing network.
In Proceedings of the International Joint Conference on Neural
Networks (IJCNN), volume 2, pages 531-537, Piscataway, NJ. IEEE Service
Center.
- Chang and Juang, 1992
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Chang, P.-C. and Juang, B.-H. (1992).
Discriminative template training for dynamic programming speech
recognition.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 1, pages 493-496,
San Francisco,USA.
- Cho and Kim, 1995
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Cho, S.-B. and Kim, J. H. (1995).
An HMM/MLP architecture for sequence recognition.
Neural Computation, 7:358-369.
- Chou et al., 1992
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Chou, W., Juang, B., and Lee, C. (1992).
Segmental GPD training of HMM based speech recognizer.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 1, pages 473-476,
San Francisco,USA.
- Cottrell and Fort, 1987
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Cottrell, M. and Fort, J.-C. (1987).
Étude d'un processus d'auto-organisation.
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- Cottrell et al., 1997
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Cottrell, M., Fort, J.-C., and Pages, G. (1997).
Theoretical aspects of the SOM algorithm.
In Workshop on Self-Organizing Maps, pages 246-267, Espoo,
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Digalakis, V., Monaco, P., and Murveit, H. (1996).
Genones: Generalized mixture tying in continuous hidden Markov
model-based speech recognizers.
IEEE Transactions on Speech and Audio Processing,
4(4):281-289.
- Dugast et al., 1994
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Dugast, C., Devillers, L., and Aubert, X. (1994).
Combining TDNN and HMM in a hybrid system for improved
continuous-speech recognition.
IEEE Transactions on Speech and Audio Processing,
2(1):217-223.
- Erwin et al., 1992a
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Erwin, E., Obermayer, K., and Schulten, K. (1992a).
Self-organizing maps: Ordering, convergence properties and energy
functions.
Biological Cybernetics, 67(1):47-55.
- Erwin et al., 1992b
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Erwin, E., Obermayer, K., and Schulten, K. (1992b).
Self-organizing maps: Stationary states, metastability and
convergence rate.
Biological Cybernetics, 67(1):35-45.
- Feller, 1966
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Feller, W. (1966).
An Introduction to Probability Theory and its Applications,
volume II.
John Wiley & Sons Inc., New York.
- Flanagan, 1996
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Flanagan, J. A. (1996).
Self-organisation in Kohonen's SOM.
Neural Networks, 9(7):1185-1197.
- Forney, 1973
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Forney, G. D. (1973).
The Viterbi algorithm.
Proceedings of the IEEE, 61(3):268-278.
- Fort and Pages, 1996
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Fort, J.-C. and Pages, G. (1996).
About the Kohonen algorithm: Strong or weak self-organization?
Neural Networks, 9(5):773-785.
- Franzini et al., 1990
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Franzini, M., Lee, K.-F., and Waibel, A. (1990).
Connectionist Viterbi training: a new hybrid method for continuous
speech recognition.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 1, pages 425-428,
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Friedman, J., Baskett, F., and Shustek, L. (1975).
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A competitive algorithm for training HMM for speech recognition.
In Proceedings of 4th European Conference on Speech
Communication and Technology, pages 2187-2190, Madrid, Spain.
- Gauvain and Lee, 1994
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Gauvain, J.-L. and Lee, C.-H. (1994).
Maximum a posteriori estimation for multivariate Gaussian mixture
observations of Markov chains.
IEEE Transactions on Speech and Audio Processing,
2(2):291-298.
- Gillick and Cox, 1989
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Gillick, L. and Cox, S. (1989).
Some statistical issues in the comparison of speech recognition
algorithms.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), pages 532-535, Glasgow,
Scotland.
- Gorin and Mammone, 1994
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Gorin, A. and Mammone, R. J. (1994).
Introduction to the special issue on neural networks for speech
processing.
IEEE Transactions on Speech and Audio Processing,
2(1):113-114.
- Hämäläinen, 1995
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Hämäläinen, A. (1995).
Self-organizing Map and Reduced Kernel Density Estimation.
PhD thesis, University of Jyväskylä, Jyväskylä, Finland.
- Holmström and Hämäläinen, 1993
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Holmström, L. and Hämäläinen, A. (1993).
The self-organizing reduced kernel density estimator.
In Proceedings of the International Conference on Neural
Networks (ICNN), pages 417-421, Piscataway, NJ. IEEE Service Center.
- Huang and Lippmann, 1991
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Huang, W. Y. and Lippmann, R. P. (1991).
HMM speech recognition with neural net discrimination.
In Lippmann, R. P., Moody, J. E., and Touretzky, D. S., editors,
Advances in Neural Information Processing Systems 3, pages 194-202. Morgan
Kaufmann, San Mateo, CA.
- Huang and Jack, 1989
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Huang, X. and Jack, M. (1989).
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Computer Speech and Language, 3(3):239-252.
- Huo et al., 1995
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Huo, Q., Chan, C., and Lee, C.-H. (1995).
Bayesian adaptive learning of the parameters of hidden Markov model
for speech recognition.
IEEE Transactions on Speech and Audio Processing,
3(5):334-345.
- Iwamida et al., 1990
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Iwamida, H., Katagiri, S., McDermott, E., and Tohkura, Y. (1990).
A hybrid speech recognition system using HMMs with an LVQ-trained
codebook.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 1, pages 489-492.
- Jalanko, 1980
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Jalanko, M. (1980).
Studies of Learning Projective Methods in Automatic Speech
Recognition.
PhD thesis, Helsinki University of Technology, Espoo, Finland.
- Jelinek, 1976
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Jelinek, F. (1976).
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Proceedings of the IEEE, 64(4):532-536.
- Jelinek and Mercer, 1980
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Jelinek, F. and Mercer, R. (1980).
Interpolated estimation of Markov source parameters from sparse
data.
In Proceedings of an International Workshop on Pattern
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- Juang, 1985
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Juang, B.-H. (1985).
Maximum likelihood estimation for mixture multivariate stochastic
observation of Markov chains.
AT&T Technical Journal, 64(6):1235-1249.
- Juang and Katagiri, 1992
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Juang, B.-H. and Katagiri, S. (1992).
Discriminative learning for minimum error classification.
IEEE Transactions on Signal Processing, 40(12):3043-3054.
- Juang and Rabiner, 1990
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Juang, B.-H. and Rabiner, L. R. (1990).
The segmental K-means algorithm for estimating parameters of hidden
Markov models.
IEEE Transactions on Acoustics, Speech, and Signal Processing,
38(9):1639-1641.
- Juang and Rabiner, 1991
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Juang, B.-H. and Rabiner, L. R. (1991).
Hidden Markov models for speech recognition.
Technometrics, 33(3):251-272.
- Kangas, 1995
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Kangas, J. (1995).
Increasing the error tolerance in transmission of vector quantized
images by self-organizing maps.
In Fogelman-Soulié, F. and Gallinari, P., editors,
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Networks, volume 1, pages 287-291. EC2 et Cie.
- Kapadia et al., 1993
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Kapadia, S., Valtchev, V., and Young, S. (1993).
MMI training for continuous phoneme recognition on the TIMIT
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In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 2, pages 491-494.
- Kaski, 1997
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Kaski, S. (1997).
Data Exploration Using Self-Organizing Maps.
PhD thesis, Helsinki University of Technology, Espoo, Finland.
- Kasslin et al., 1992
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Kasslin, M., Kangas, J., and Simula, O. (1992).
Process state monitoring using self-organizing maps.
In Aleksander, I. and Taylor, J., editors, Artificial Neural
Networks, 2, volume I, pages 1532-1534, Amsterdam, Netherlands.
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- Katagiri and Lee, 1993
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Katagiri, S. and Lee, C.-H. (1993).
A new hybrid algorithm for speech recognition based on HMM
segmentation and learning vector quantization.
IEEE Transactions on Speech and Audio Processing,
1(4):421-430.
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Katagiri, S., Lee, C.-H., and Juang, B.-H. (1991).
New discriminative training algorithms based on the generalized
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In Proceedings of the IEEE Workshop on Neural Networks for
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Kim, D.-S., Lee, S.-Y., Han, M.-S., Lee, C.-H., Park, J.-G., and Suh, S.-W.
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Multi-dimensional HMM parameter estimation using self-organizing
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In Proceedings of the 3rd International Conference on Fuzzy
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Kohonen, T. (1982).
Clustering, taxonomy, and topological maps of patterns.
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Kohonen, T. (1986a).
Dynamically expanding context, with application to the correction of
symbol strings in recognition of continuous speech.
In Proceedings of the 8th International Conference on Pattern
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- Kohonen, 1986b
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Kohonen, T. (1986b).
Learning vector quantization for pattern recognition.
Report TKK-F-A601, Helsinki University of Technology, Espoo, Finland.
- Kohonen, 1990a
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Kohonen, T. (1990a).
Improved versions of learning vector quantization.
In Proceedings of the International Joint Conference on Neural
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- Kohonen, 1990b
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Kohonen, T. (1990b).
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Proceedings of the IEEE, 78(9):1464-1480.
- Kohonen, 1991
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Kohonen, T. (1991).
Workstation-based phonetic typewriter.
In Proceedings of the IEEE Workshop on Neural Networks for
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- Kohonen, 1992
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Kohonen, T. (1992).
New developments of learning vector quantization and the
self-organizing map.
In Symposium on Neural Networks; Alliances and Perspectives in
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- Kohonen, 1993
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Kohonen, T. (1993).
Things you haven't heard about the self-organizing map.
In Proceedings of the International Conference on Neural
Networks (ICNN), pages 1147-1156, Piscataway, NJ. IEEE Service Center.
- Kohonen, 1995
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Kohonen, T. (1995).
Self-Organizing Maps.
Springer, Berlin.
- Kohonen, 1996
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Kohonen, T. (1996).
The speedy SOM.
Technical Report A33, Helsinki University of Technology, Laboratory
of Computer and Information Science, Espoo, Finland.
- Kohonen et al., 1988
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Kohonen, T., Barna, G., and Chrisley, R. (1988).
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Networks (ICNN), volume I, pages 61-68, Los Alamitos, CA. IEEE Computer
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Kohonen, T., Hynninen, J., Kangas, J., and Laaksonen, J. (1996a).
SOM_PAK: the self-organizing map programming package.
Report A31, Helsinki University of Technology, Laboratory of Computer
and Information Science, Espoo, Finland.
Program package available via WWW at URL
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- Kohonen et al., 1996b
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Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J., and Torkkola, K. (1996b).
LVQ_PAK: the learning vector quantization programming package.
Report A30, Helsinki University of Technology, Laboratory of Computer
and Information Science, Espoo, Finland.
Program package available via WWW at URL
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Kohonen, T., Kaski, S., Lagus, K., and Honkela, T. (1996c).
Very large two-level SOM for the browsing of newsgroups.
In von der Malsburg, C., von Seelen, W., Vorbrüggen, J. C., and
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on Artificial Neural Networks, Bochum, Germany, July 16-19, 1996, Lecture
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Kohonen, T., Oja, E., Simula, O., Visa, A., and Kangas, J. (1996d).
Engineering application of the self-organizing map.
Proceedings of the IEEE, 84(10):1358-1384.
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Koikkalainen, P. (1995).
Fast deterministic self-organizing maps.
In Fogelman-Soulié, F. and Gallinari, P., editors,
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Koikkalainen, P. and Oja, E. (1990).
Self-organizing hierarchical feature maps.
In Proceedings of the International Joint Conference on Neural
Networks (IJCNN), volume II, pages 279-284, Piscataway, NJ. IEEE Service
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- Komori and Katagiri, 1992
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Komori, T. and Katagiri, S. (1992).
GPD training of dynamic programming-based speech recognizers.
J. Acoustical Society of Japan, 13(6):341-349.
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An efficient output probability computation for continuous HMM
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In Proceedings of 4th European Conference on Speech
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- Kurimo, 1992
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Kurimo, M. (1992).
Combinations of adaptive vector quantization methods and hidden
Markov models in speech recognition.
Master's thesis, Helsinki University of Technology, Espoo, Finland.
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- Kurimo, 1994
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Kurimo, M. (1994).
Application of learning vector quantization and self-organizing maps
for training continuous density and semi-continuous Markov models.
Licentiate's Thesis, Helsinki University of Technology, Espoo,
Finland.
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Kurimo, M. (1997).
SOM based density function approximation for mixture density
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In Workshop on Self-Organizing Maps, pages 8-13, Espoo,
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Kurimo, M. and Torkkola, K. (1992a).
Application of SOMs and LVQ in training continuous density hidden
Markov models.
In Proceedings of the International Conference on Spoken
Language Processing, volume 1, pages 543-546, Banff, Canada.
- Kurimo and Torkkola, 1992b
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Kurimo, M. and Torkkola, K. (1992b).
Combining LVQ with continuous density hidden Markov models in
speech recognition.
In Proceedings of the SPIE's Conference on Neural and Stochastic
Methods in Image and Signal Processing, volume 1766, pages 726-734, San
Diego, USA.
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Lampinen, J. and Oja, E. (1989).
Fast self-organization by the probing algorithm.
In Proceedings of the International Joint Conference on Neural
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Lee, C.-H., Lin, C.-H., and Juang, B.-H. (1990).
A study on speaker adaptation of continuous density HMM parameters.
In Proceedings of the IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), volume 1, pages 145-148.
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Liporace, L. A. (1982).
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IEEE Transactions on Information Theory, 28(5):729-734.
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Neural Computation, 1(1):1-38.
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Estimation of hidden Markov model parameters by minimizing
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In Proceedings of the IEEE International Conference on
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Lopez-Gonzalo, E. and Hernandez-Gomez, L. A. (1993).
Fast vector quantization using neural maps for CELP at 2400 bps.
In Proceedings of 3rd European Conference on Speech
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Makino, S., Endo, M., Sone, T., and Kido, K. (1992).
Recognition of phonemes in continuous speech using a modified LVQ2
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McDermott, E. (1990).
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probabilistic descent training for various speech units.
Computer Speech and Language, 8(4):351-368.
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Mizuta, S. and Nakajima, K. (1990).
An optimal discriminative training method for continuous mixture
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Smoothing HMMs by means of a SOM.
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Rabiner, L. R. (1989).
A tutorial on hidden Markov models and selected applications in
speech recognition.
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IEEE Transactions on Speech and Audio Processing,
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Connectionist optimization of tied mixture hidden Markov models.
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Ritter, H. (1989).
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Report A9, Helsinki University of Technology, Laboratory of Computer
and Information Science, Espoo, Finland.
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Ritter, H. (1991).
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IEEE Transactions on Neural Networks, 2(1):173-175.
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On the stationary state of Kohonen's self-organizing sensory
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Biological Cybernetics, 54(1):99-106.
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Ritter, H. and Schulten, K. (1988).
Convergence properties of Kohonen's topology preserving maps:
fluctuations, stability, and dimension selection.
Biological Cybernetics, 60(1):59-71.
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- Segura et al., 1994
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Segura, J., Rubio, A., Peinado, A., Garcia, P., and Roman, R. (1994).
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Seide, F. (1995).
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tree-based nearest neighbor search.
In Proceedings of 4th European Conference on Speech
Communication and Technology, pages 1079-1082, Madrid, Spain.
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