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, fundamental frequency.
4. Spectrum, spectrogram, spectral analysis of vowels and consonants.
5. Cepstrum, cepstral analysis, Mel frequency cepstral coefficients and other speech parameters.
6. Introduction to classification, SOM, k-NN, GMM, ANN and classifier fusion.
7. Speaker identification (SI) and possible approaches.
8. Speech emotion recognition (SER), stress recognition.
9. Automatic speech recognition (ASR) and possible approaches.
10. Text to speech (TTS), speech corpora and open-source projects.
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 recognition system - GMM, ANN.
8. Example of project proposal.
9. Speech synthesis.
10. 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, fundamental frequency.
4. Spectrum, spectrogram, spectral analysis of vowels and consonants.
5. Cepstrum, cepstral analysis, Mel frequency cepstral coefficients and other speech parameters.
6. Introduction to classification, SOM, k-NN, GMM, ANN and classifier fusion.
7. Speaker identification (SI) and possible approaches.
8. Speech emotion recognition (SER), stress recognition.
9. Automatic speech recognition (ASR) and possible approaches.
10. Text to speech (TTS), speech corpora and open-source projects.
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 recognition system - GMM, ANN.
8. Example of project proposal.
9. Speech synthesis.
10. Presentation of projects.