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Course Catalog 2010-2011
SGN-4106 Speech Recognition, 5 cr |
Person responsible
Konsta Koppinen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Final examination and exercises.
Principles and baselines related to teaching and learning
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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 | ||
| 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-4010 Speech Processing Methods | Mandatory |
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 |
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Additional information
Suitable for postgraduate studies
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |