Course detail

Speech Signal Processing (in English)

FIT-ZREeAcad. year: 2022/2023

Aplikace počítačového zpracování řeči, číslicové zpracování řečových signálů, tvorba a slyšení řeči, úvod do fonetiky, předzpracování a základní parametry, lineárně-prediktivní model, cepstrum, určování základního tónu hlasu, kódování - časová oblast a vokodéry, rozpoznávání - DTW a HMM, syntéza. Software a knihovny pro zpracování řeči.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Dr. Ing. Jan Černocký

Department

Department of Computer Graphics and Multimedia (UPGM)

Offered to foreign students

Of all faculties

Learning outcomes of the course unit

The students will get familiar with basic characteristics of speech signal in relation to production and hearing of speech by humans. They will understand basic algorithms of speech analysis common to many applications. They will be given an overview of applications (recognition, synthesis, coding) and be informed about practical aspects of speech algorithms implementation. The students will be able to design a simple system for speech processing (speech activity detector, recognizer of limited number of isolated words), including its implementation into application programs.

Prerequisites

Solid knowledge of basic mathematics and signal processing (Fourier transform, linear filtering, random signals).

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • mid-term test
  • presentation of projects
  • presentation of results in computer labs

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

To provide students with the knowledge of basic characteristics of speech signal in relation to production and hearing of speech by humans. To describe basic algorithms of speech analysis common to many applications. To give an overview of applications (recognition, synthesis, coding) and to inform about practical aspects of speech algorithms implementation.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Gold, B., Morgan, N.: Speech and Audio Signal Processing, John Wiley & Sons, 2000, ISBN 0-471-35154-7 (EN)
Psutka, J.: Komunikace s počítačem mluvenou řečí. Academia, Praha, 1995, ISBN 80-200-0203-0 (EN)
(EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-MGR-1H Master's

    branch MGH , 1 year of study, summer semester, recommended course

  • Programme IT-MSC-2 Master's

    branch MGMe , 1 year of study, summer semester, compulsory

  • Programme MIT-EN Master's 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. František Grézl, Ph.D.

Syllabus

  • Introduction, applications of speech processing, sciences relevant for SP, informational content of speech.
  • Digital processing of speech signals.
  • Speech production and perception, basic notions from psycho-acoustics, applications in speech processing.
  • Introduction to phonetics, international norms for phoneme mark-up.
  • Pre-processing and basic parameters of speech.
  • Linear-predictive model, spectrum using LP, applications of LP.
  • Cepstral analysis, Mel-frequency cepstrum.
  • Determination of fundamental frequency.
  • Speech coding
  • Speech recognition - dynamic programming DTW, hidden Markov models HMM
  • Speech synthesis
  • Software and libraries for speech processing.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. František Grézl, Ph.D.

Syllabus

    Except the last one, Matlab is used in labs.
  • Frames, windows, spectrum, pre-processing.
  • Linear prediction (LPC).
  • Fundamental frequency estimation.
  • Coding.
  • Recognition - Dynamic time Warping (DTW).
  • Recognition - hidden Markov models (Hidden Markov Model Toolkit - HTK).

Elearning

eLearning: currently opened course