P124 Speech signal processing

Faculty of Informatics
Autumn 2001
Extent and Intensity
2/1. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
doc. Dr. Ing. Jan Černocký (lecturer), doc. RNDr. Ivan Kopeček, CSc. (deputy)
Guaranteed by
prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing - Faculty of Informatics
Contact Person: doc. RNDr. Ivan Kopeček, CSc.
Thu 9:00–10:50 C525, Thu 11:00–12:50 B117
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Applications of speech processing, digital processing of speech signals, production and perception of speech, introduction to phonetics, pre-processing and basic parameters of speech, linear-predictive model, cepstrum, fundamental frequency estimation, coding - time domain and vocoders, recognition - DTW and HMM, synthesis.
  • Informational contents of written and spoken form of speech.
  • Techniques of signal processing applied to speech: Fourier transform, z-transform, linear filtering.
  • Time domain and frequency domain behavoir of linear systems.
  • Signal processing model of speech production.
  • Excitation and filter.
  • Determination of parameters using linear prediction.
  • LPC coefficients and derived parameters (PARCOR, LAR,...).
  • Speech analysis using short-time Fourier transform (STFT): filter-bank interpretation, computation using fast Fourier transform (FFT).
  • Cepstral analysis.
  • Parameterization with perceptually warped frequency axis.
  • Fundamental frequency determination.
  • Features for speech processing, criteria of choice.
  • Measures of similarity between speech segments.
  • Speech coding: waveform and parametric vocoders.
  • Excitation modeling (CELP).
  • Phonetic vocoders.
  • Speech recognition: Hidden Markov Models (HMM).
  • HMM training and HMM decoding.
  • Extension of HMMs to continuous speech recognition.
  • Statistical language models.
  • The studied methods are experimentally exercised in computer laboratories (Matlab).
  • PSUTKA, Josef. Komunikace s počítačem mluvenou řečí. Praha: Academia, 1995. 287 s. ISBN 8020002030. info
  • RABINER, Lawrence R. and Biing-Hwang JUANG. Fundamentals of speech recognition. Englewood Cliffs: Prentice Hall PTR, 1993. xxxv, 507. ISBN 0-13-015157-2. info
Assessment methods (in Czech)
tydne 2h prednaska. 2h pocitacovych cviceni 1x za 14 dni. Maly domaci projekt, presentace na posledni prednasce. Test v poc. laboratorich, pisemna zkouska.
Language of instruction
Further Comments
The course is taught annually.
Teacher's information
The course is also listed under the following terms Autumn 1999, Autumn 2000.
  • Enrolment Statistics (recent)
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