Semester.ly

Johns Hopkins University | EN.520.666

Information Extraction

3.0

credits

Average Course Rating

(4.55)

Introduction to statistical methods of speech recognition (automatic transcription of speech) and understanding. The course is a natural continuation of EN.601.465 but is independent of it. Topics include elementary probability theory, hidden Markov models, and n-gram models using maximum likelihood, Bayesian and discriminative methods, and deep learning techniques for acoustic and language modeling. Recommended Course Background: EN.550.310 AND EN.600.120 or equivalent, expertise in Matlab or Python programming.

Spring 2013

(4.57)

Spring 2015

(4.71)

Spring 2023

(4.38)

Spring 2013

Professor: Sanjeev Khudanpur

(4.57)

The best aspects of the course included the useful content and material learned, as well as the assignments and projects which helped students understand many of the topics. The worst aspects of the course included the outdated research materials and hefty workload. The students felt that the course was also too focused on speech. The course would improve if there was a better textbook and more updated research materials. Prospective students should know that the course is chal enging and that a good knowledge of probability and statistics is helpful.

Spring 2015

Professor: Sanjeev Khudanpur

(4.71)

The best aspects included the relatable and effective professor, the 1-on-1 feedback sessions, and the obvious student progress made throughout the semester. Many students agreed that the method of working on different iterations of the same speech was an effective approach, and that the professor’s guidance was very helpful. Some students felt the semester course was too short and would have benefited from a full semester of work. Suggestions for improvement included having opportunities for individual presentations. Prospective students should be prepared for a significant amount of speech preparation every week and the opportunity to improve public speaking and presentation skills.

Spring 2023

Professor: Sanjeev Khudanpur

(4.38)