Generalisation and discrimination emerge from a self-organising componential network: A speech example

Network: Computation in Neural Systems

Volumn 8, Issue 4, 1997, Pages 425-440

  Webber, Chris J S ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY LABORATORY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0039475178     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X_8_4_005     Document Type: Article

References (31)

1. Barlow H B 1989 Unsupervised learning Neural Comput. 1 295-311
2. Bridle J S 1990 Alpha-nets: a recurrent 'neural' network architecture with a hidden Markov model interpretation Speech Commun. 9 83-92
3. Carpenter G A and Grossberg S 1988 The adaptive resonance theory of adaptive pattern recognition by a self-organising neural network Computer (March) 77-88
4. Dayan P and Hinton G 1996 Varieties of Helmholtz machine Neural Networks 9 1385-403
5. Field D J 1994 What is the goal of sensory coding? Neural Comput. 6 559-601
6. Foldiak P 1990 Forming sparse representations by local anti-Hebbian learning Biol. Cybern. 64 165-70
7. Friedman J H and Tukey J W 1974 A projection pursuit algorithm for exploratory data analysis IEEE Trans. Comput. C-23 881-90
8. Fukushima K and Miyake S 1982 Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position Pattern Recogn. 15 455-69
9. Hebb D O 1949 The Organisation of Behavior (New York: Wiley)
10. Hinton G and Zemel R 1994 Autoencoders, minimum description-length and Helmholtz free energy Advances in Neural Information Processing Systems vol 6, ed J Cowan et al (San Mateo, CA: Morgan Kaufmann) pp 3-10
11. Huber P J 1985 Projection pursuit Ann. Stat. 13 435-75
12. Intrator N 1992 Feature extraction using an unsupervised neural network Neural Comput. 4 98-107
13. Iwamida H, Katagiri S, McDermott E and Tohkura Y 1990 A hybrid speech recognition system using HMMs with an LVQ-trained codebook 1990 Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (Albuquerque, NM, April 1990) (Piscataway, NJ: IEEE) pp 489-92
14. Kohonen T 1982 Clustering taxonomy and topological maps of patterns Proc. 6th IEEE Int. Conf. on Pattern Recognition (Munich, October 1982) (Piscataway, NJ: IEEE) pp 114-28
15. Kohonen T, Torkkola K, Shozakai M, Kangas J and Ventä O 1988 Phonetic typewriter for Finnish and Japanese Proc. 1988 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (New York, April 1988) (Piscataway, NJ: IEEE) pp 607-10
16. Luttrell S P 1992 Self-supervised adaptive networks IEE Proc. F 139 371-7
17. McDermott E and Katagiri S 1989 Shift-invariant, multi-category phoneme recognition using Kohonen's LVQ2 Proc. 1989 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (Glasgow, May 1989) (Piscataway, NJ: IEEE) pp 81-4
18. Nowell P and Moore R K 1995 The application of dynamic programming techniques to non-word based topic spotting Proc. 4th Eur. Conf. on Speech Communications and Technology (Madrid, September 1995) ed J M Pardo et al (ESCA) pp 1355-8
19. Oja E 1982 A simplified neuron model as a principal component analyser J. Math. Biol. 15 267-73
20. Olshausen B A and Field D J 1996 Emergence of simple-cell receptive field properties by learning a sparse code for natural images Nature 381 607-9
21. Robinson A J 1994 An application of recurrent nets to phone probability estimation IEEE Trans. Neural Networks 5 229-39
22. Rumelhart D E, Hinton G E and Williams R J 1986 Learning internal representations by error propagation Parallel Distributed Processing Vol 1 (Cambridge, MA: MIT Press)
23. Russell M J, Moore R K, Tomlinson M J and Deacon J C A 1983 RSRE speech database recordings. Part II: Recordings made for automatic speech recognition assessment and research RSRE report 84008, DRA, Malvern UK
24. Sankoff D and Kruskal J B 1983 An anthology of algorithms and concepts for sequence comparison Time Warps, String Edits, and Macromolecules: the Theory and Practice of Sequence Comparison (Reading, MA: Addison-Wesley)
25. Saund E 1994 A multiple cause mixture model for unsupervised learning Neural Comput. 7 57-71
26. Skilling A I, Nowell P and Moore R K 1994 Acoustic based topic-spotting, unpublished report
27. Tattersall G D and Johnston R D 1984 Self-organising arrays for speech recognition Proc. Inst. Acoust. 6 323-31
28. Webber C J S 1994 Self-organisation of transformation-invariant detectors for constituents of perceptual patterns Network: Comput. Neural Syst. 5 471-96
29. Yu G, Russel W, Schwartz R and Makhoul J 1990 Discriminant analysis and supervised vector quantisation for continuous speech recognition Proc. 1990 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (Albuquerque, NM, April 1990) (Piscataway, NJ: IEEE) pp 685-8
30. Zemel R 1993 A minimum description-length framework for unsupervised learning PhD Thesis University of Toronto
31. Ziv J and Lempel A 1977 A universal algorithm for sequential data compression, IEEE Trans. Inf. Theory IT-23 337-43