Speech Signal Processing

• 26 hrs lectures
• 2 hrs exercises
• 12 hrs pc labs
• 12 hrs projects

• 51 pts final exam (written part)
• 14 pts mid-term test (written part)
• 6 pts labs
• 29 pts projects

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.

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.

• Psutka, J.: Komunikace s počítačem mluvenou řečí. Academia, Praha, 1995, ISBN  80-200-0203-0
• Gold, B., Morgan, N.: Speech and Audio Signal Processing, John Wiley & Sons, 2000, ISBN 0-471-35154-7
• Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition, Signal Processing, Prentice Hall, Engelwood Cliffs, NJ, 1993, ISBN 0-13-015157-2

• Psutka, J.: Komunikace s počítačem mluvenou řečí. Academia, Praha, 1995, ISBN  80-200-0203-0 

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. 

1. Parameterization, DTW, HMM.

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).

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