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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 state-of-the-art 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 (N-grams). 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 Estimation-Maximization (EM) algorithm
- Statistically based discriminant (linear and nonlinear discriminant
functions)
- Artificial neural networks
- Vector quantization (K-means, 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, McGraw-Hill.
- Duda, R., and Hart, P. (1973) Pattern Classification and Scene
Analysis, Wiley-Interscience.
- Therrien, C. (1989) Decision, Estimation and Classification, John
Wilwey.
- Jelinek, F. (1998) Statistical Method for Speech Recognition,
MIT Press, Cambridge Massachusetts.
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