Course detail

Speech Signal Processing

FIT-ZREAcad. year: 2020/2021

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. Software and libraries for speech processing.

Guarantor

prof. Dr. Ing. Jan Černocký

Department

Department of Computer Graphics and Multimedia (UPGM)

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

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Literature

Gold, B., Morgan, N.: Speech and Audio Signal Processing, Wiley-Interscience; 2 edition.
Rabiner, L. R., & Schafer, R. W. Theory and applications of digital speech processing, Pearson, 2011.
Psutka, J., Müller, L., Matoušek, J., & Radová, V., Mluvíme s počítačem česky, Academia, 2006.
Yu, D., Deng, L., Automatic speech recognition, Springer, 2016.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • mid-term test 14 pts
  • project 29 pts
  • presentation of results in computer labs 6 pts

Language of instruction

Czech, English

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
    branch MIS , any year of study, summer semester, 5 credits, elective
    branch MBS , any year of study, summer semester, 5 credits, elective
    branch MIN , any year of study, summer semester, 5 credits, compulsory-optional
    branch MMM , any year of study, summer semester, 5 credits, elective

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, elective
    specialization NBIO , any year of study, summer semester, 5 credits, elective
    specialization NGRI , any year of study, summer semester, 5 credits, elective
    specialization NNET , any year of study, summer semester, 5 credits, elective
    specialization NVIZ , any year of study, summer semester, 5 credits, elective
    specialization NCPS , any year of study, summer semester, 5 credits, elective
    specialization NSEC , any year of study, summer semester, 5 credits, elective
    specialization NEMB , any year of study, summer semester, 5 credits, elective
    specialization NHPC , any year of study, summer semester, 5 credits, elective
    specialization NISD , any year of study, summer semester, 5 credits, elective
    specialization NIDE , any year of study, summer semester, 5 credits, elective
    specialization NISY , any year of study, summer semester, 5 credits, elective
    specialization NMAL , any year of study, summer semester, 5 credits, elective
    specialization NMAT , any year of study, summer semester, 5 credits, elective
    specialization NSEN , any year of study, summer semester, 5 credits, elective
    specialization NVER , any year of study, summer semester, 5 credits, elective
    specialization NSPE , any year of study, summer semester, 5 credits, compulsory

  • Programme IT-MGR-2 Master's

    branch MGM , 1. year of study, summer semester, 5 credits, compulsory
    branch MSK , 2. year of study, summer semester, 5 credits, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. Dr. Ing. Jan Černocký

Syllabus

  1. Introduction, applications of speech processing. 
  2. Digital processing of speech signals.
  3. Speech production and its signal processing model. 
  4. Pre-processing and basic parameters of speech, cepstrum.
  5. Linear-predictive model. 
  6. Fundamental frequency estimation.
  7. Speech coding - basics
  8. CELP Speech coding. 
  9. Speech recognition - basics, DTW. 
  10. Hidden Markov models HMM. 
  11. Large vocabulary continuous speech recognition (LVCSR) systems. 
  12. Speaker and language recognition. Neural networks in speech processing. 
  13. Text to speech synthesis. 

Fundamentals seminar

2 hours, compulsory

Teacher / Lecturer

Ing. Jan Brukner

Syllabus

  1. Parameterization, DTW, HMM.

Exercise in computer lab

12 hours, compulsory

Teacher / Lecturer

Ing. Jan Brukner

Syllabus

    Except the last one, Matlab is used in labs.
  1. Introduction. 
  2. Linear prediction and vector quantization. 
  3. Fundamental frequency estimation and speech coding. 
  4. Basics of classification. 
  5. Recognition - Dynamic time Warping (DTW).
  6. Recognition - hidden Markov models (HTK).

Project

12 hours, compulsory

Teacher / Lecturer

Ing. Jan Brukner