New book: NEURAL NETWORKS FOR SPEECH AND SEQUENCE RECOGNITION

[Yoshua Bengio]

NEW BOOK!

NEURAL NETWORKS FOR SPEECH AND SEQUENCE RECOGNITION

Yoshua BENGIO

Learning algorithms for sequential data are crucial
in many applications, in fields such as speech recognition, 
time-series prediction, control and signal monitoring.
This book applies the techniques of artificial neural
networks, in particular recurrent networks, time-delay
networks, convolutional networks, and hidden Markov
models, using real world examples. Highlights
include basic elements for the practical application 
of back-propagation and back-propagation through time,
integrating domain knowledge and learning from
examples, and hybrids of neural networks with hidden
Markov models.



International Thomson Computer Press
ISBN 1-85032-170-1

This book is available at bookstores near you, or
from the publisher:

In the US: US$52.95
800-842-3636, fax 606-525-7778, or 800-865-5840, fax 606-647-5013

In Canada: CA$73.95
416-752-9100 ext 444, fax 416-752-9646

On the Internet: 
http://www.thomson.com/itcp.html
http://www.thomson.com/orderinfo.html
americas-info at list.thomson.com (in the Americas)
row-info at list.thomson.com (rest of the World)


Contents

1 Introduction
  1.1 Connectionist Models
  1.2 Learning Theory

2 The Back-Propagation Algorithm
  2.1 Introduction to Back-Propagation
  2.2 Formal Description
  2.3 Heuristics to Improve Convergence and Generalization
  2.4 Extensions

3 Integrating Domain Knowledge and Learning from Examples
  3.1 Automatic Speech Recognition
  3.2 Importance of Pre-processing Input Data
  3.3 Input Coding
  3.4 Input Invariances
  3.5 Importance of Architecture Constraints on the Network
  3.6 Modularization
  3.7 Output Coding

4 Sequence Analysis
  4.1 Introduction
  4.2 Time Delay Neural Networks
  4.3 Recurrent Networks
  4.4 BPS
  4.5 Supervision of a Recurrent Network Does Not Need to Be Everywhere
  4.6 Problems with Training of Recurrent Networks
  4.7 Dynamic Programming Post-Processors
  4.8 Hidden Markov Models

5 Integrating ANNs with Other Systems
  5.1 Advantages and Disadvantages of Current Algorithms for ANNs
  5.2 Modularization and Joint Optimization

6 Radial Basis Functions and Local Representation
  6.1 Radial Basis Functions Networks
  6.2 Neurobiological Plausibility
  6.3 Relation to Vector Quantization, Clustering, and Semi-Continuous HMMs
  6.4 Methodology
  6.5 Experiments on Phoneme Recognition with RBFs

7 Density Estimation with a Neural Network
  7.1 Relation Between Input PDF and Output PDF
  7.2 Density Estimation
  7.3 Conclusion

8 Post-Processors Based on Dynamic Programming
  8.1 ANN/DP Hybrids
  8.2 ANN/HMM Hybrids
  8.3 ANN/HMM Hybrid: Phoneme Recognition Experiments
  8.4 ANN/HMM Hybrid: Online Handwriting Recognition Experiments

References

Index



-- 
Yoshua Bengio 
Professeur Adjoint, Dept. Informatique et Recherche Operationnelle
Pavillon Andre-Aisenstadt #3339 , Universite de Montreal, 
Dept. IRO, CP 6128, Succ. Centre-Ville,
2920 Chemin de la tour, Montreal, Quebec, Canada, H3C 3J7

E-mail: bengioy at iro.umontreal.ca      Fax:       (514) 343-5834
web: http://www.iro.umontreal.ca/htbin/userinfo/user?bengioy
or http://www.iro.umontreal.ca/labs/neuro/
Tel: (514) 343-6804. Residence: (514) 738-6206