Course detail
Speech Signal Processing
FIT-ZREAcad. year: 2022/2023
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.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- mid-term test 14 pts
- project 29 pts
- presentation of results in computer labs 6 pts
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Psutka, J.: Komunikace s počítačem mluvenou řečí. Academia, Praha, 1995, ISBN 80-200-0203-0
www stránka předmětu https://www.fit.vutbr.cz/study/courses/ZRE/public/
Recommended reading
Psutka, J., Müller, L., Matoušek, J., & Radová, V., Mluvíme s počítačem česky, Academia, 2006.
Rabiner, L. R., & Schafer, R. W. Theory and applications of digital speech processing, Pearson, 2011.
Yu, D., Deng, L., Automatic speech recognition, Springer, 2016.
Elearning
Classification of course in study plans
- Programme IT-MSC-2 Master's
branch MBS , 0 year of study, summer semester, elective
branch MGM , 1 year of study, summer semester, compulsory
branch MIS , 0 year of study, summer semester, elective
branch MMM , 0 year of study, summer semester, elective - Programme MITAI Master's
specialization NADE , 0 year of study, summer semester, elective
specialization NBIO , 0 year of study, summer semester, elective
specialization NCPS , 0 year of study, summer semester, elective
specialization NEMB , 0 year of study, summer semester, elective
specialization NGRI , 0 year of study, summer semester, elective
specialization NHPC , 0 year of study, summer semester, elective
specialization NIDE , 0 year of study, summer semester, elective
specialization NISD , 0 year of study, summer semester, elective
specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
specialization NMAL , 0 year of study, summer semester, elective
specialization NMAT , 0 year of study, summer semester, elective
specialization NNET , 0 year of study, summer semester, elective
specialization NSEC , 0 year of study, summer semester, elective
specialization NSEN , 0 year of study, summer semester, elective
specialization NSPE , 0 year of study, summer semester, compulsory
specialization NVER , 0 year of study, summer semester, elective
specialization NVIZ , 0 year of study, summer semester, elective
specialization NISY , 0 year of study, summer semester, elective - Programme IT-MSC-2 Master's
branch MBI , 0 year of study, summer semester, compulsory-optional
branch MIN , 0 year of study, summer semester, compulsory-optional
branch MPV , 0 year of study, summer semester, compulsory-optional
branch MSK , 2 year of study, summer semester, compulsory-optional - Programme MITAI Master's
specialization NEMB up to 2021/22 , 0 year of study, summer semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction, applications of speech processing.
- Digital processing of speech signals.
- Speech production and its signal processing model.
- Pre-processing and basic parameters of speech, cepstrum.
- Linear-predictive model.
- Fundamental frequency estimation.
- Speech coding - basics
- CELP Speech coding.
- Speech recognition - basics, DTW.
- Hidden Markov models HMM.
- Large vocabulary continuous speech recognition (LVCSR) systems.
- Speaker and language recognition. Neural networks in speech processing.
- Text to speech synthesis.
Fundamentals seminar
Teacher / Lecturer
Syllabus
- Parameterization, DTW, HMM.
Exercise in computer lab
Teacher / Lecturer
Syllabus
- Except the last one, Matlab is used in labs.
- Introduction.
- Linear prediction and vector quantization.
- Fundamental frequency estimation and speech coding.
- Basics of classification.
- Recognition - Dynamic time Warping (DTW).
- Recognition - hidden Markov models (HTK).
Elearning