Subject syllabus
1. Introduction to subject and speech processing, practical applications and its using.
2. Speech production, basic concepts, speech preprocessing (DC Offset, preemphases, segmentation, windowing).
3. Basic features - energy, zero cross ratio (ZCR), Jitter, Shimmer, autocorrelation.
4. Speech signal analysis - extraction of fundamental frequency F0 and its using, recognition of voiced and unvoiced consonants.
5. Spectrum, spectrogram, spectral analysis of vowels and consonants.
6. Cepstrum, cepstral analysis, Mel frequency cepstral coefficients and other speech parameters.
7. Introduction to classification, SOM, k-NN, GMM, ANN and classifier fusion.
8. Speaker identification (SI) and possible approaches.
9. Speech emotion recognition (SER), stress recognition.
10. Automatic speech recognition (ASR) and possible approaches.
11. Hidden Markov Model (HMM), structure, training and using for speech recognition (Viterbi algorithm and token-passing).
12. Speech synthesis and vocoder.
13. Text to speech (TTS), speech corpora and open-source projects.
14. Actual trends in speech processing..
Excercise syllabus
1. Introduction, Safety, Conditions for subject completion
2. Practical exercises – speech preprocessing – DC offset, preemphases, segmentation, windowing
3. Practical exercises – Feautures extraction – energy, zero cross ratio, fundamental frequency
4. Practical exercises – Spectral analysis of speech signal
5. Practical exercises – Features extraction – MFCC, LPC
6. Test and assigment of project
7. Design of speaker identification system - GMM, ANN
8. Example of project proposal
9. Design of speech emotion recognition system. - GMM, ANN
10. Design of automatic speech recognition system - DTW, HMM
11. Speech synthesis
12. Classifier fusion
13. Presentation of projects
1. Introduction to subject and speech processing, practical applications and its using.
2. Speech production, basic concepts, speech preprocessing (DC Offset, preemphases, segmentation, windowing).
3. Basic features - energy, zero cross ratio (ZCR), Jitter, Shimmer, autocorrelation.
4. Speech signal analysis - extraction of fundamental frequency F0 and its using, recognition of voiced and unvoiced consonants.
5. Spectrum, spectrogram, spectral analysis of vowels and consonants.
6. Cepstrum, cepstral analysis, Mel frequency cepstral coefficients and other speech parameters.
7. Introduction to classification, SOM, k-NN, GMM, ANN and classifier fusion.
8. Speaker identification (SI) and possible approaches.
9. Speech emotion recognition (SER), stress recognition.
10. Automatic speech recognition (ASR) and possible approaches.
11. Hidden Markov Model (HMM), structure, training and using for speech recognition (Viterbi algorithm and token-passing).
12. Speech synthesis and vocoder.
13. Text to speech (TTS), speech corpora and open-source projects.
14. Actual trends in speech processing..
Excercise syllabus
1. Introduction, Safety, Conditions for subject completion
2. Practical exercises – speech preprocessing – DC offset, preemphases, segmentation, windowing
3. Practical exercises – Feautures extraction – energy, zero cross ratio, fundamental frequency
4. Practical exercises – Spectral analysis of speech signal
5. Practical exercises – Features extraction – MFCC, LPC
6. Test and assigment of project
7. Design of speaker identification system - GMM, ANN
8. Example of project proposal
9. Design of speech emotion recognition system. - GMM, ANN
10. Design of automatic speech recognition system - DTW, HMM
11. Speech synthesis
12. Classifier fusion
13. Presentation of projects