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
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
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
2 hours, compulsory
Teacher / Lecturer
Syllabus
- Parameterization, DTW, HMM.
Exercise in computer lab
12 hours, compulsory
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).