Fundamentals of Speech Recognition and Generation - 2/1 Autumn Seminar NPFL038
Algorithms for Speech Recognition - 2/2 Spring Course NPFL079
Natural computing for learning and optimisation - 2/1 Autumn Course NPFL107
NPFL038 Fundamentals
of Speech Recognition and Generation - 2/1 Autumn Seminar
This
course deals with speech recognition and generation tasks and feature
extraction of voice and utterance characteristics. Of particular
interest will be topics related to Hidden Markov Models as applied to
speech (FFT, n-dimensional clustering, Gaussian mixtures, parameter
value extraction from data, phonetic representation, prosodic
analysis etc.). Preparation and training of own speech models.
HTK Software - Hidden Markov Model Toolkit - The HTK Book
Markov Models for Pattern Recognition, Gernot A. Fink, Springer-Verlag Berlin Heidelberg, 2008
X. Huang, A. Acero, H. Hon, Spoken Language Processing, Prentice-Hall, 2001
Auditory Demonstrations , A.J.M. Houtsma, T.D.Rossing, W.M. Wagenaars
IPA Table with Sounds , Peter Ladefoged, A Course in Phonetics
Zdena Palková, Fonetika a fonologie češtiny, Karolinum, 1994
Tomáš Duběda, Jazyky a jejich zvuky, Karolinum, 2005
NPFL079 Algorithms
for Speech Recognition - 2/2 Spring Course
The course
presents recent methodologies and software toolkits for speech
recognition. Students will learn how to develop systems of automatic
speech recognition and transcription, computer dialogue systems and
speaker identification. The course shows principles, preparation and
decoding algorithms of statistical acoustic and language models (HMM,
n-gram and structured language models, final state transducers,
graphical models, Viterbi dynamic programming, heuristic hypothesis
search strategies, stack decoder).
F. Jelinek, Statistical Methods for Speech Recognition, MIT Press, 1998
J. Psutka, L. Müller, J. Matoušek, V. Radová, Mluvíme s počítačem česky, Academia, 2006
X. Huang, A. Acero, H. Hon, Spoken Language Processing, Prentice-Hall, 2001
Daniel Jurafsky, James H. Martin, Speech and Language Processing, Prentice-Hall, 2000
Computer Speech & Language,
Journals, Elsevier Ltd.
ScienceDirect
Online - for Charles University
Speech Communication, Journals,
Elsevier Ltd.
ScienceDirect
Online - for Charles University
NPFL107 Natural
computing for learning and optimisation - 2/1 Autumn Course
The course offers introduction into some parts of
nature-inspired computing. The topics of the course are
self-organisation in nature and artificial systems, swarm
intelligence algorithms, social insects colonies organisation.
Organisms can co-operate to achieve certain tasks, their methods are
effective in general optimisation and learning tasks. The aim of the
course is to show a collection of these algorithms, and examine their
components and their behavior.
Fresh study material - page link.
Literature:D. Corne, A. Reynolds, E. Bonabeau (2010). Swarm Intelligence, in Handbook of Natural Computing (G. Rozenberg, T. Back, J.N. Kok, eds.), vol. II: Broader Perspective. Springer D. W.
Corne, K. Deb, J. Knowles, X. Yao (2010). Selected Applications of Natural Computing , (G. Rozenberg, T. Back, J.N. Kok, eds.), vol. II: Broader Perspective. Springer
Blum, C. and Li, X. , Swarm Intelligence in Optimization, in Blum, C. and Merkle, D. (eds.), Swarm Intelligence - Introduction and Applications, Springer, 2008: 43-85, 2008
M. Dorigo, M. Birattari, and T. Stützle (2006). Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique, IEEE Computational Intelligence Magazine, November:28-39.
X.-S. Yang and S. Deb (2010). Engineering Optimisation by Cuckoo Search, International Journal of Mathematical Modelling and Numerical Optimisation, 1(4):330-343.
Last updated: 13/10/2012