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Course number and title: EE214B Advanced Topics in Speech Processing
Credits: 4
Instructor(s)-in-charge: Abeer Alwan (alwan@ee.ucla.edu)
Course type: Lecture
Required or Elective: A Signals and Systems course.
Course Schedule:
Lecture: 3 hrs/week.
Lab: 2 hrs/week.
Outside Study: 7 hrs/week.
Office Hours: 2 hrs/week.
 
Course Assessment:
Homework: Several homework assignments.
Exams: 1 midterm and 1 final examination.
Design: 2 computer projects.
 
Grading Policy: Typically, 30% homework and computer assignments, 30% midterm, 40% final.
Course Prerequisites: EEM214A.
Catalog Description: Advanced techniques used in various speech-processing applications, with focus on speech recognition by humans and machine. Physiology and psychoacoustics of human perception. Dynamic Time Warping (DTW) and Hidden Markov Models (HMM) for automatic speech recognition systems, pattern classification, and search algorithms. Aids for hearing impaired.  
Textbook and any related course material:
Rabiner and Juang, Fundamentals of Speech Recognition, Prentice Hall, NJ, 1993.
 
Course Website
Topics covered in the course:
Acoustic Theory of Speech Production.
Homomorphic (Cepstral) Signal Processing: MFCC and LPCC.
Spectral, Cepstral, and LPC Distortion Metrics.
Introduction to Hidden Markov Models.
Will this course involve computer assignments? YES Will this course have TA(s) when it is offered? NO

:: Last modified: February 2013 by J. Lin ::

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