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5.0
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1,000
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99%
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
EESC 6366 John Hansen | |||||
A- | |||||
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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
EESC 6366 John Hansen | |||||
A- | |||||
Grades: 118
Median GPA: A-
Mean GPA: 3.495
4.1
Professor rating
3.4
Difficulty
7
Ratings given
86%
Would take again
Speech and Speaker Recognition
EESC 6366
Erik Jonsson School of Engineering and Computer Science
Introduction to concepts in automatic recognition methods for speech applications; the primary emphasis is for automatic speech recognition and speaker identification techniques. Topics include speech features for recognition, hidden Markov models (HMMs) for acoustic and language applications (speech recognition, dialect/language recognition), Gaussian mixture models (GMMs) for speaker characterization, robustness issues to address noise and channel conditions for automatic recognition. 3 credit hours.
Offering Frequency: Each year
Grades: 63
Median GPA: A-
Mean GPA: 3.450
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Grades: 118
Median GPA: A-
Mean GPA: 3.495
4.1
Professor rating
3.4
Difficulty
7
Ratings given
86%
Would take again
Speech and Speaker Recognition
EESC 6366
Erik Jonsson School of Engineering and Computer Science
Introduction to concepts in automatic recognition methods for speech applications; the primary emphasis is for automatic speech recognition and speaker identification techniques. Topics include speech features for recognition, hidden Markov models (HMMs) for acoustic and language applications (speech recognition, dialect/language recognition), Gaussian mixture models (GMMs) for speaker characterization, robustness issues to address noise and channel conditions for automatic recognition. 3 credit hours.
Offering Frequency: Each year
Grades: 63
Median GPA: A-
Mean GPA: 3.450
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