Popis predmeta

Course Description

Course gives fundamentals of digital speech processing and its applications in communications and multimedia. Digital speech modeling, parametric models. Speech analysis, parameter estimation for vocal tract model and excitation model. Most important speech models and their properties. Speech coding and applications. Automatic speech and speaker recognition, language detection. Speech feature vectors, Cepstral analysis. Statistical models for speech recognition, Hidden Markov Model, Gaussian Mixture Model, training procedures for statistical models. Acoustical and lexical models. Speech synthesis, diphonic, threephonic. Speech normalization and modification. Examples of systems for speech coding, recognition and synthesis.

Learning Outcomes

  1. recognize the significance of digital speech processing and its applications
  2. describe speech production mechanism and corresponding physical models
  3. compare various methods for modeling of speech signal in continuous and discrete time domain
  4. apply linear prediction methods for modeling of speech signal
  5. employ homomorphic speech processing for estimation of excitation and vocal tract model
  6. develop simple algorithms for speech processing using Matlab
  7. analyze quantization effects of model coefficients on its accuracy
  8. apply methods for recognition of vowels and speaker identity

Forms of Teaching

Lectures

Lectures are organized in two terms. First term consists of 7 weeks of lectures and midterm exam. The second term consists of another 6 weeks of lectures and final exam. Weekly workload for lectures is 2 hours for total of 15 weeks in semester.

Independent assignments

Total course workload related to student individual work amounts to 90 hours, which students use for Program exercises and preparation for exams. Homework for each of the two semester terms is the Report of individual work on Program exercises. This Report also includes the report of Laboratory exercises. For individual work, students have to examine corresponding chapters in Course-book and Lecture notes which are cited in the week-by-week plan, perform the required exercises and prepare the report for each chapter.

Laboratory

During the semester, laboratory exercises are organized in accordance to week-by-week plan. These exercises are used to prepare students for individual work.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 10 % 0 % 10 %
Homeworks 0 % 20 % 0 % 20 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Final Exam: Oral 10 %
Exam: Written 50 % 60 %
Exam: Oral 10 %
Comment:

Assessment of Laboratory exercises and Homework (Program exercises) is performed commonly based on submitted Reports of individual work for the first and second term. Students can approach the oral part of the final exam only if they have at least 50% of total points from midterm exam and written part of the final exam.

Week by Week Schedule

  1. Lectures: (L): Introduction to digital speech processing and its applications, Automatic speech, speaker and language recognition, Basic principles of speech synthesis, Text-to-Speech, Computer dialog systems with applications in virtual reality; Lab.exc. (E): Chap.: Survey of digital speech processing applications, Chap.: Fundamentals of speech production, Chap.: Phonetics and Linguistics.
  2. Lectures (L): Fundamentals of speech production, Physical model of production; Lab.exc. (E): Chap 1: Recording of speech signals using sound cards.
  3. Lectures (L): Acoustic model of vocal tract; Lab.exc. (E): Chap. 2: Analysis of speech signals in time domain.
  4. Lectures (L): Excitation signal of the vocal tract; Lab.exc. (E): Chap. 3: Spectral analysis of speech signals and spectrograms and Chap. 4: Analysis of speech formant structure.
  5. Lectures (L): Connected tube model of the vocal tract, Time discrete vocal tract model; Lab.exc. (E): Chap. 5: Automatic classification of vowels based on their format structure.
  6. Lectures (L): Linear prediction and its application for speech modeling; Lab.exc. (E): Chap. 6: Automatic speaker classification based on formant structure.
  7. Lectures (L): Autocorrelation method for LPC model estimation; Lab.exc. (E): Chap. 7: Linear prediction methods.
  8. Midterm exam
  9. Lectures (L): Properties of autocorrelation based LPC model; Lab.exc. (E): Chap. 8: Autocorrelation method for speech predictor estimation; and Chap. 9: Levinson-Durbin algorithm; prediction gain analysis.
  10. Lectures (L): Covariance method for LPC model estimation, Parametric representations for short-time speech spectral envelope modeling; Lab.exc. (E): Chap. 10: Covariance method for speech predictor estimation.
  11. Lectures (L): Homomorphic speech processing; Lab.exc. (E): Chap. 11: Quantization effects of LPC predictor coefficients.
  12. Lectures (L): Applications of homomorphic processing on speech signal; Lab.exc. (E): Chap. 12: Homomorphic analysis of speech signal.
  13. Lectures (L): Introduction to automatic speech recognition (ASR), Speech analysis for ASR; Lab.exc. (E): Chap. 13: Voicing and pitch estimation.
  14. Lectures (L): Feature vectors; Statistical models and classification methods for ASR; Lab.exc. (E): Chap. 14: Example of the Vocoder.
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (1. semester) (3. semester)
Communication and Space Technologies (profile)
Free Elective Courses (1. semester) (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Science (profile)
Free Elective Courses (1. semester) (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (1. semester) (3. semester)
Data Science (profile)
Free Elective Courses (1. semester) (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (1. semester) (3. semester)
Electronic and Computer Engineering (profile)
Elective Courses of the Profile (1. semester) (3. semester)
Electronics (profile)
Free Elective Courses (1. semester) (3. semester)
Information and Communication Engineering (profile)
Elective Courses of the Profile (1. semester) Elective Coursesof the Profile (3. semester)
Network Science (profile)
Free Elective Courses (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (1. semester) (3. semester)

Literature

(.), Petrinović, D. (2010.), Uvod u digitalnu obradbu govora koristenjem Matlaba, FER, Udžbenici sveučilišta u Zagrebu,
Petrinović, D. (2003.), Laboratorijske vježbe iz digitalne obrade govora, FER, ZESOI
John R. Deller, Jr., John H. L. Hansen, John G. Proakis (2000.), Discrete-Time Processing of Speech Signals, Wiley-IEEE Press
Panos E. Papamichalis (1987.), Practical Approaches to Speech Coding, Prentice Hall
A. M. Kondoz (2005.), Digital Speech, John Wiley & Sons
Petrinović, D. (2010.), Uvod u digitalnu obradbu govora korištenjem Matlaba, FER, Udžbenici sveučilišta u Zagrebu
Petrinović, D. (2010.), Digitalna obrada govora, Zavodska skripta, FER, ZESOI
Lawrence R. Rabiner, Biing-Hwang Juang (1993.), Fundamentals of Speech Recognition, Prentice Hall
W. Bastiaan Kleijn, Kuldip K. Paliwal (1995.), Speech Coding and Synthesis, Elsevier Science Limited
L.R.Rabiner, R.W.Schafer (1978.), Digital Processing of Speech Signals, Prentice-Hall
E. Keller (1994.), Fundamentals of Speech Synthesis and Speech Recognition, Wiley-Blackwell
Sadaoki Furui (1991.), Advances in Speech Signal Processing, CRC Press

Learning Outcomes

  1. recognize the significance of digital speech processing and its applications
  2. describe speech production mechanism and corresponding physical models
  3. compare various methods for modeling of speech signal in continuous and discrete time domain
  4. apply linear prediction methods for modeling of speech signal
  5. employ homomorphic speech processing for estimation of excitation and vocal tract model
  6. develop simple algorithms for speech processing using Matlab
  7. analyze quantization effects of model coefficients on its accuracy
  8. apply methods for recognition of vowels and speaker identity

Forms of Teaching

Lectures

Lectures are organized in two terms. First term consists of 7 weeks of lectures and midterm exam. The second term consists of another 6 weeks of lectures and final exam. Weekly workload for lectures is 2 hours for total of 15 weeks in semester.

Independent assignments

Total course workload related to student individual work amounts to 90 hours, which students use for Program exercises and preparation for exams. Homework for each of the two semester terms is the Report of individual work on Program exercises. This Report also includes the report of Laboratory exercises. For individual work, students have to examine corresponding chapters in Course-book and Lecture notes which are cited in the week-by-week plan, perform the required exercises and prepare the report for each chapter.

Laboratory

During the semester, laboratory exercises are organized in accordance to week-by-week plan. These exercises are used to prepare students for individual work.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 10 % 0 % 10 %
Homeworks 0 % 20 % 0 % 20 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Final Exam: Oral 10 %
Exam: Written 50 % 60 %
Exam: Oral 10 %
Comment:

Assessment of Laboratory exercises and Homework (Program exercises) is performed commonly based on submitted Reports of individual work for the first and second term. Students can approach the oral part of the final exam only if they have at least 50% of total points from midterm exam and written part of the final exam.

Week by Week Schedule

  1. Lectures: (L): Introduction to digital speech processing and its applications, Automatic speech, speaker and language recognition, Basic principles of speech synthesis, Text-to-Speech, Computer dialog systems with applications in virtual reality; Lab.exc. (E): Chap.: Survey of digital speech processing applications, Chap.: Fundamentals of speech production, Chap.: Phonetics and Linguistics.
  2. Lectures (L): Fundamentals of speech production, Physical model of production; Lab.exc. (E): Chap 1: Recording of speech signals using sound cards.
  3. Lectures (L): Acoustic model of vocal tract; Lab.exc. (E): Chap. 2: Analysis of speech signals in time domain.
  4. Lectures (L): Excitation signal of the vocal tract; Lab.exc. (E): Chap. 3: Spectral analysis of speech signals and spectrograms and Chap. 4: Analysis of speech formant structure.
  5. Lectures (L): Connected tube model of the vocal tract, Time discrete vocal tract model; Lab.exc. (E): Chap. 5: Automatic classification of vowels based on their format structure.
  6. Lectures (L): Linear prediction and its application for speech modeling; Lab.exc. (E): Chap. 6: Automatic speaker classification based on formant structure.
  7. Lectures (L): Autocorrelation method for LPC model estimation; Lab.exc. (E): Chap. 7: Linear prediction methods.
  8. Midterm exam
  9. Lectures (L): Properties of autocorrelation based LPC model; Lab.exc. (E): Chap. 8: Autocorrelation method for speech predictor estimation; and Chap. 9: Levinson-Durbin algorithm; prediction gain analysis.
  10. Lectures (L): Covariance method for LPC model estimation, Parametric representations for short-time speech spectral envelope modeling; Lab.exc. (E): Chap. 10: Covariance method for speech predictor estimation.
  11. Lectures (L): Homomorphic speech processing; Lab.exc. (E): Chap. 11: Quantization effects of LPC predictor coefficients.
  12. Lectures (L): Applications of homomorphic processing on speech signal; Lab.exc. (E): Chap. 12: Homomorphic analysis of speech signal.
  13. Lectures (L): Introduction to automatic speech recognition (ASR), Speech analysis for ASR; Lab.exc. (E): Chap. 13: Voicing and pitch estimation.
  14. Lectures (L): Feature vectors; Statistical models and classification methods for ASR; Lab.exc. (E): Chap. 14: Example of the Vocoder.
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (1. semester) (3. semester)
Communication and Space Technologies (profile)
Free Elective Courses (1. semester) (3. semester)
Computational Modelling in Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Computer Science (profile)
Free Elective Courses (1. semester) (3. semester)
Control Systems and Robotics (profile)
Free Elective Courses (1. semester) (3. semester)
Data Science (profile)
Free Elective Courses (1. semester) (3. semester)
Electrical Power Engineering (profile)
Free Elective Courses (1. semester) (3. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (1. semester) (3. semester)
Electronic and Computer Engineering (profile)
Elective Courses of the Profile (1. semester) (3. semester)
Electronics (profile)
Free Elective Courses (1. semester) (3. semester)
Information and Communication Engineering (profile)
Elective Courses of the Profile (1. semester) Elective Coursesof the Profile (3. semester)
Network Science (profile)
Free Elective Courses (1. semester) (3. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (1. semester) (3. semester)

Literature

(.), Petrinović, D. (2010.), Uvod u digitalnu obradbu govora koristenjem Matlaba, FER, Udžbenici sveučilišta u Zagrebu,
Petrinović, D. (2003.), Laboratorijske vježbe iz digitalne obrade govora, FER, ZESOI
John R. Deller, Jr., John H. L. Hansen, John G. Proakis (2000.), Discrete-Time Processing of Speech Signals, Wiley-IEEE Press
Panos E. Papamichalis (1987.), Practical Approaches to Speech Coding, Prentice Hall
A. M. Kondoz (2005.), Digital Speech, John Wiley & Sons
Petrinović, D. (2010.), Uvod u digitalnu obradbu govora korištenjem Matlaba, FER, Udžbenici sveučilišta u Zagrebu
Petrinović, D. (2010.), Digitalna obrada govora, Zavodska skripta, FER, ZESOI
Lawrence R. Rabiner, Biing-Hwang Juang (1993.), Fundamentals of Speech Recognition, Prentice Hall
W. Bastiaan Kleijn, Kuldip K. Paliwal (1995.), Speech Coding and Synthesis, Elsevier Science Limited
L.R.Rabiner, R.W.Schafer (1978.), Digital Processing of Speech Signals, Prentice-Hall
E. Keller (1994.), Fundamentals of Speech Synthesis and Speech Recognition, Wiley-Blackwell
Sadaoki Furui (1991.), Advances in Speech Signal Processing, CRC Press