
Outline
Many of the language and speech processing technologies use statistical
formulations based on important basic concepts of statistical pattern
classification. As a matter of fact, current stateoftheart speech recognition
systems are based on stochastic models, which parameters are automatically
trained on large corpora of acoustic and text databases. More specifically,
speech units (words, syllable or phones) are usually represented in terms of
Hidden Markov Models (HMM), a particular case of stochastic finite state
automaton, while the syntactical constraints are usually approximated by
stochastic grammars (Ngrams). Consequently, this topic area covers the basic
concepts and theories underlying statistical pattern classification and that
will be necessary to students to understand the techniques underlying the
prevailing approaches to language and speech processing.
Material on the application of statistical pattern classification to Automatic
Speech Recognition is also covered under the Language Engineering Applications
area.
Topics
 Statistical pattern recognition
 Bayes'rule, minimum error classification
 Maximum a posteriori and maximum likelihood classification
 Likelihood EstimationMaximization (EM) algorithm
 Statistically based discriminant (linear and nonlinear discriminant
functions)
 Artificial neural networks
 Vector quantization (Kmeans, KNN, etc)
 Decision trees, regression trees
 Feature extraction and analysis (linear discriminant analysis, principal
component analysis)
 Stochastic finite state automata and discrete Markov models
 Definition and operation of stochastic finite state automata
 Discrete Markov models (parametrization and probability estimation)
 Examples of applications
 Hidden Markov models (HMM)
 Definition, parametrization and hypotheses
 Estimation of model probabilities (Viterbi and forward recurrences),
dynamic programming
 Estimation of HMM parameters
 HMMs for classification of (piecewise stationary) temporal sequences
References
 Bishop, Christopher M. (1995) Neural Networks for Pattern
Recognition. Oxford University Press.
 Papoulis, A. (1965) Probability, Random Variables, and Stochastic
Processes, McGrawHill.
 Duda, R., and Hart, P. (1973) Pattern Classification and Scene
Analysis, WileyInterscience.
 Therrien, C. (1989) Decision, Estimation and Classification, John
Wilwey.
 Jelinek, F. (1998) Statistical Method for Speech Recognition,
MIT Press, Cambridge Massachusetts.
Overview Course Content Members Participate Search Questions School
Theoretical Linguistics Natural Language Processing Phonetics and Phonology Cognitive Models for Speech Language Processing Speech Signal Processing Pattern Recognition Language Engineering Applications
