|
|
|||||||||||||||||
Course Catalog 2011-2012
SGN-4106 Speech Recognition, 5 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Tuomas Virtanen
Requirements
Final examination and exercises.
Principles and baselines related to teaching and learning
-
Learning outcomes
After completing this course, the student will have a firm grasp of the basic techniques used in speech recognition. Specifically, he or she will understand and be able to implement the front-end used for extracting relevant information from the speech signal, as well as a detailed understanding of the mathematical principles of hidden Markov models (used to model the data provided courtesy of the front-end). Teaches the basics of automated speech recognition, particularly the use of hidden Markov models and neural networks.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Front-end of a speech recognizer, cepstral coefficients, phonetics | ||
| 2. | Training of Hidden Markov models (HMM) | ||
| 3. | Adaptation methods | ||
| 4. | Language models |
Evaluation criteria for the course
Exam and exercises.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Lecture slides | Bryan Pellom | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-2500 Johdatus hahmontunnistukseen | Advisable | 1 |
| SGN-2506 Introduction to Pattern Recognition | Advisable | 1 |
| SGN-4010 Puheenkäsittelyn menetelmät | Mandatory |
1 . Prior knowledge about pattern recognition is advisable.
Additional information about prerequisites
SGN-4010 Speech Processing or corresponding knowledge of speech processing is required.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
|
|
|