CTU FEE Moodle
Speech processing
B232 - Summer 23/24
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Speech processing - AE2M31ZRE
Main course
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | English |
| Extent of teaching | 2P+2C |
Annotation
The subject is devoted to basis of speech processing addressed to students of master program with special focus on multimedia applications. Discussed speech technology is currently applied in many systems in different fields (e.g. information dialogue systems, voice controlled devices, dictation systems or transcription of audio-video recordings, support for language teaching, etc.). Further information can be found at http://noel.feld.cvut.cz/vyu/a2m31zre and at http://moodle.kme.feld.cvut.cz
Study targets
The goals of the subject is to introduce used speech technology in the most important multimedia applications. Students should manage the knowledge as basic characteristics of speech signal, speech enhancement, speech recognition, speech synthesis, audio-visual speech processing, etc. Students will practice basic tasks of speech processing in MATLAB environment and also other publicly available tools for speech analysis will be used. As a homework, students will elaborate semester project which will be presented at the exercise according to planned schedule.
Course outlines
1. Introduction - speech signal (digital form), speech production model
2. Basic characteristics of speech signal, phonetic and articulatory aspects
3. Spectral characteristics of speech signal (DFT and LPC spectrum)
4. Noise suppression in speech signal (additive and convolution noise, one-channel, multi-channel)
5. Hearing aids and cochlear implants (anatomy and hearing model, speech processing)
6. Principles of speech recognition, basic tasks ad applications
7. Feature extraction for speech recognition
8. Small vocabulary speech recognition based on DTW and HMM (HTK)
9. Dictation and transcription systems (large vocabulary speech recognition)
10. Speaker verification and identification.
11. Speech synthesis - basic principles (concatenative and formant synthesis, PSOLA)
12. Audio-visual speech recognition
13. Multimedia systems with voice input (dialog systems, logopaedy, language teaching)
14. Language recognition. Reserve.
2. Basic characteristics of speech signal, phonetic and articulatory aspects
3. Spectral characteristics of speech signal (DFT and LPC spectrum)
4. Noise suppression in speech signal (additive and convolution noise, one-channel, multi-channel)
5. Hearing aids and cochlear implants (anatomy and hearing model, speech processing)
6. Principles of speech recognition, basic tasks ad applications
7. Feature extraction for speech recognition
8. Small vocabulary speech recognition based on DTW and HMM (HTK)
9. Dictation and transcription systems (large vocabulary speech recognition)
10. Speaker verification and identification.
11. Speech synthesis - basic principles (concatenative and formant synthesis, PSOLA)
12. Audio-visual speech recognition
13. Multimedia systems with voice input (dialog systems, logopaedy, language teaching)
14. Language recognition. Reserve.
Exercises outlines
1. Introduction: speech signal, tools for analysis, sources of speech signals
2. Basic time-domain characteristics: energy, intensity, zero-crossing, fundamental frequency
3. Spectral characteristics: short-time DFT and LPC spectrum, spectrogram
4. Suppression of additive noise in speech signal
5. Convolutory noise suppression
6. Speech processing for hearing aids and cochlear implants
7. Cepstrum and cepstral distance: voice activity detection, features for recognition
8. DTW based recognition: simple recognizer of particular words
9. HMM based recognition: basic tasks and demonstration of HMM modelling
10. Speaker verification based on GMM
11. Speech synthesis: implementation of formant synthesis, demonstration of available tools
12. Semester work presentations
13. Semester work presentations
14. Reserve. Credits
2. Basic time-domain characteristics: energy, intensity, zero-crossing, fundamental frequency
3. Spectral characteristics: short-time DFT and LPC spectrum, spectrogram
4. Suppression of additive noise in speech signal
5. Convolutory noise suppression
6. Speech processing for hearing aids and cochlear implants
7. Cepstrum and cepstral distance: voice activity detection, features for recognition
8. DTW based recognition: simple recognizer of particular words
9. HMM based recognition: basic tasks and demonstration of HMM modelling
10. Speaker verification based on GMM
11. Speech synthesis: implementation of formant synthesis, demonstration of available tools
12. Semester work presentations
13. Semester work presentations
14. Reserve. Credits
Literature
[1] Rabiner, L., Schafer, R. W.: Introduction to Digital Speech Processing Foundations and Trends in Signal Processing). Now Publishers Inc, 2007.
[2] Huang, X., Acero, A., Hon, H.-W.: Spoken Language Processing. Prentice Hall, 2001.
[3] Deller Jr., J. R., Hansen, J. H. L., Proakis, J. G.: Discrete-time Processing of Speech Signals. Wiley, 2000.
[4] McLoughlin, I.: Applied Speech and audio Processing: With Matlab Examples. Cambridge University Press, 2009.
[5] Jelinek, F.: Statistical Methods for Speech Recognition (Language, Speech, and Communication). The MIT Press, 1998.
[6] ITU-T Recommendations - http://www.itu.int/ITU-T
[2] Huang, X., Acero, A., Hon, H.-W.: Spoken Language Processing. Prentice Hall, 2001.
[3] Deller Jr., J. R., Hansen, J. H. L., Proakis, J. G.: Discrete-time Processing of Speech Signals. Wiley, 2000.
[4] McLoughlin, I.: Applied Speech and audio Processing: With Matlab Examples. Cambridge University Press, 2009.
[5] Jelinek, F.: Statistical Methods for Speech Recognition (Language, Speech, and Communication). The MIT Press, 1998.
[6] ITU-T Recommendations - http://www.itu.int/ITU-T
Requirements
Bases of digital signal processing are supposed as preliminary knowledge.
Speech technology in telecommunications - AE2M31RAT
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | English |
| Extent of teaching | 2P+2C |
Annotation
The subject is devoted to basis of speech processing addressed to students of master program with special focus on communication applications as speech technology has currently many applications in communication systems. Further information can be found at http://noel.feld.cvut.cz/vyu/ae2m31rat . Detailed information for registered students can be found at teaching portal http://moodle.kme.feld.cvut.cz .
Study targets
The goals of the subject is to introduce used speech technology in the most important communication applications. Students should manage the knowledge as basic characteristics of speech signal, speech coding, speech enhancement, speech recognition, speech synthesis, etc. Students will practice basic tasks of speech processing in MATLAB environment and also other publicly available tools for speech analysis will be used. As a homework, students will elaborate semester project which will be presented at the exercise according to planned schedule.
Course outlines
1. Introduction - speech signal, basic characteristics, speech production model
2. Digitalization and basic coding strategies (PCM, ADPCM, a-law)
3. Spectral characteristics of speech signal (DFT a LPC spectrum, LSF a LSP)
4. Vocoders used in telecommunications (RPE-LTP, CELP, ACELP)
5. Methods of noise suppression for speech signals (channel and acoustic noises, VAD)
6. Echo cancellation in speech signal
7. Measurement of speech quality (subjective and objective methods)
8. Principles of speech recognition: basic tasks, feature extraction, DTW algorithm
9. Small vocabulary recognizer based on HMM (HTK toolkit)
10. Speaker recognition: verification and identification.
11. Speech synthesis - basic principles (concatenative and formant synthesis, PSOLA)
12. Voice controlled dialogue communication systems
13. Packet loss concealment for speech transmitted via communication channel
14. Further application of speech processing in communication systems. Reserve
2. Digitalization and basic coding strategies (PCM, ADPCM, a-law)
3. Spectral characteristics of speech signal (DFT a LPC spectrum, LSF a LSP)
4. Vocoders used in telecommunications (RPE-LTP, CELP, ACELP)
5. Methods of noise suppression for speech signals (channel and acoustic noises, VAD)
6. Echo cancellation in speech signal
7. Measurement of speech quality (subjective and objective methods)
8. Principles of speech recognition: basic tasks, feature extraction, DTW algorithm
9. Small vocabulary recognizer based on HMM (HTK toolkit)
10. Speaker recognition: verification and identification.
11. Speech synthesis - basic principles (concatenative and formant synthesis, PSOLA)
12. Voice controlled dialogue communication systems
13. Packet loss concealment for speech transmitted via communication channel
14. Further application of speech processing in communication systems. Reserve
Exercises outlines
1. Introduction: speech signal, tools for analysis, sources of speech signals
2. Basic time-domain characteristics: energy, intensity, zero-crossing, fundamental frequency
3. Spectral characteristics: short-time DFT and LPC spectrum, spectrogram
4. LPC based vocoder: implementation of particular functional blacks
5. Suppression of additive noise in speech signal
6. Echo cancellation
7. Cepstrum and cepstral distance: voice activity detection, features for recognition
8. DTW based recognition: simple recognizer of particular words
9. HMM based recognition: basic tasks and demonstration of HMM modelling
10. Speaker verification based on GMM
11. Speech synthesis: implementation of formant synthesis, demonstration of available tools
12. Semester work presentations
13. Semester work presentations
14. Reserve. Credits
2. Basic time-domain characteristics: energy, intensity, zero-crossing, fundamental frequency
3. Spectral characteristics: short-time DFT and LPC spectrum, spectrogram
4. LPC based vocoder: implementation of particular functional blacks
5. Suppression of additive noise in speech signal
6. Echo cancellation
7. Cepstrum and cepstral distance: voice activity detection, features for recognition
8. DTW based recognition: simple recognizer of particular words
9. HMM based recognition: basic tasks and demonstration of HMM modelling
10. Speaker verification based on GMM
11. Speech synthesis: implementation of formant synthesis, demonstration of available tools
12. Semester work presentations
13. Semester work presentations
14. Reserve. Credits
Literature
[1] Rabiner, L., Schafer, R. W.: Introduction to Digital Speech Processing Foundations and Trends in Signal Processing). Now Publishers Inc, 2007.
[2] Huang, X., Acero, A., Hon, H.-W.: Spoken Language Processing. Prentice Hall, 2001.
[3] Deller Jr., J. R., Hansen, J. H. L., Proakis, J. G.: Discrete-time Processing of Speech Signals. Wiley, 2000.
[4] McLoughlin, I.: Applied Speech and audio Processing: With Matlab Examples. Cambridge University Press, 2009.
[5] Jelinek, F.: Statistical Methods for Speech Recognition (Language, Speech, and Communication). The MIT Press, 1998.
[6] ITU-T Recommendations - http://www.itu.int/ITU-T
[2] Huang, X., Acero, A., Hon, H.-W.: Spoken Language Processing. Prentice Hall, 2001.
[3] Deller Jr., J. R., Hansen, J. H. L., Proakis, J. G.: Discrete-time Processing of Speech Signals. Wiley, 2000.
[4] McLoughlin, I.: Applied Speech and audio Processing: With Matlab Examples. Cambridge University Press, 2009.
[5] Jelinek, F.: Statistical Methods for Speech Recognition (Language, Speech, and Communication). The MIT Press, 1998.
[6] ITU-T Recommendations - http://www.itu.int/ITU-T
Requirements
Bases of digital signal processing are supposed as preliminary knowledge.