Instructor: Prof. Hui Jiang
Time: TR10:00—11:30
Place:
BC325
Office Hour: TBA @ CSE3014 (or by appointment)
[1] Spoken Language Processing: a guide to theory, algorithm, and system development by X.D. Huang, A. Acero, H.W. Hon. (Prentice Hall PTR, ISBN 0-13-022616-5)
[2] Pattern Recognition and Machine Learning by C. M. Bishop. (Springer, ISBN 0-387-31073-8)
[3] Spoken Dialogues with Computers, edited by R. De Mori. (Academic Press, ISBN 0-12-209055-1)
[4] Foundations of Statistical Natural Language Processing, by C. D. Manning and H. Schutze. (The MIT Press, ISBN 0-262-13360-1)
[5] Pattern Classification (2nd Edition) by R. O. Duda, P. Hart and D. Stork. (John Wiley & Sons, Inc., ISBN 0-471-05669-3)
(The above reference books are optional. The course is mainly based on the lecture notes and reading assignments. The lecture notes are usually posted prior to classes)
·
Apr 13 – Project 2 has been extended one more week to April 23 and the
presentation will be held on April 25 (10am-1pm, Friday, CSB 3033).
·
Apr 1 – Porject2 has
been extended to April 16 23 and the
presentation will be held on April 18 25 (10:00am-1:00pm,
Friday, CSEB3033). Each person will have 10-15mins to present the project. The
presentation order is: Nariman, Kmiec, Nassim, Hashmat, Pourya, Li, Damon,
Feng, Hu, Vlad, Zhenyu, Talieh.
·
Mar 13 – Lecture notes for week 9 posted. This
part is closely related to option B of project 2.
·
Mar 11—Important Announcement
regarding research and project presentation:
1. Research Presentation will be held:
i) March 27th (10:00am-11:30am, Thursday, in class): Vlad & Pourya: Graphical Models; Pan & Li: WFST optimization.
ii) March 28th (10:00am-12:30pm, Friday, CSEB3033): Feng & Hu: MAP estimation of HMM; Damon & Nariman: Speaker verification; Nassim & Hashmat: Speech Understanding; Kmiec(15mins): Latent Semantic analysis; Talieh(15mins): SVM.
2. Project2 has been extended to April 9 23 and
project2 presentation will be held on April 11th 25th (10:00am-1:00pm,
Friday, CSEB3033). Each person will have 10-15mins to present the project. Option A: Nariman, Kmiec, Nassim,
Hashmat, Pourya, Li, Damon. Option B:
Feng, Hu, Vlad, Zhenyu, Talieh.
3. Meanwhile, classes on March 20, 25 and April 1 are cancelled.
·
Mar 6 – I
already had enough number (6+) of people to do option A for Project two. If you
didn’t email me any request yet, you will have to do option B for Project two.
·
Mar 6 – Project two
has been posted. Project two has two
options, please let me know your preference ASAP. Lecture notes for week eight
posted as well. Please read the HTK tutorial which is the required reading
material for this week.
·
Feb 27 – Lecture notes for week seven has been
posted.
·
Feb 26 – Reading material for week six (a
tutorial article on HMM) has been posted. The
topics for advanced study are also posted. Please identify your group
partner and topic as soon as you can.
·
Feb 20 – Lecture notes for week six has been
posted.
·
Feb 19 – Lecture notes for week five has been updated
(with WFST part added). The reading material regarding WFST is also posted.
·
Jan 31 – Project one is
posted. It is due on March 6th.
·
Jan 31 – Lecture notes for week five posted.
Reading materials for week five posted as well.
·
Jan 23 – Lecture notes for week four posted.
·
Jan 17 – Feb 11 Feb 19.
·
Jan 10 – Reading materials for weeks one and two
posted. Please catch up.
·
Jan 8 – Lecture notes for week two posted.
·
Jan 3 – Lecture notes for week one posted.
·
Jan 2 – This WWW created. The class starts from
Jan 3th.
Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10.
Evaluation:
(1)
(10%)
Assignment: basic concepts, principles, algorithms.
(2) (10%) Class participation.
(3) (55%) Two lab projects: project
one (20%) and project two (35%).
(4) (25%) Assigned article reading and class
presentation (5% out of 25% is based on your participation in evaluating
other’s presentations).
Lab Projects:
Project I : (20%) download datasets: Train.set
and Test.set.
Project II: (35%)
All topics for advanced study NEW: check here.
|
Content |
Reading Assignment |
Week 1 |
Introduction: application
background; a big picture; speech
sounds; spoken language |
|
Week 2 |
Math
Background:
probabilities; Bayes theorem; statistics; estimation; regression; hypothesis
testing; Entropy; mutual information; decision tree |
|
Week 3 |
Pattern
Classification (I): pattern classification & pattern verification;
Bayesian decision theory; |
|
Week 4 |
Pattern
Classification (II): Model estimation: maximum likelihood, Bayesian
learning, EM algorithm; Simple models: single Gaussian, Gaussian mixture
model, etc. |
|
Week 5 |
Pattern
Classification (III) & Pattern Verification:
alternative model estimation: discriminative training & Bayesian
learning; Linear discriminant functions; support vector machine (SVM); large
margin classifiers; Pattern verification as statistical hypothesis testing; speaker
verification; outlier rejection |
Tutorial on verification [C1]
(sec. 6 optional) Finite-State-Machine
(FSM) Toolkit |
Week 6 |
Hidden
Markov Model (HMM): HMM vs. Markov chains; HMM concepts; Three algorithms:
forward-backward; Viterbi decoding; Baum-Welch learning. |
|
Week 7 |
Automatic
Speech Recognition (ASR) (I): Introduction
& Acoustic modeling how to use HMM for ASR; ASR as
an example of pattern classification; Acoustic modeling: HMM learning (ML, MAP); Parameter tying (decision tree based state tying). |
|
Week 8 |
Automatic
Speech Recognition (ASR) (II): Language modeling n-grams, smoothing, learning, perplexity, class-based |
|
Week 9 |
Automatic
Speech Recognition (ASR) (III): Search Why search; Viterbi decoding in a large HMM; Beam search; Tree-based lexicon; dynamic decoding |
|
Week 10 |
Spoken
Language Processing (I): text categorization classify text documents: call/email routing, topic detection, etc. vector-based approach, Naďve Bayes classifier; Bayesian networks,
etc. (2) HMM applications: Statistical Part-of-Speech (POS) tagging; Language understanding: hidden
concept models. |
|
Week 11 |
Spoken
Language Processing (II): statistical machine translation IBM’s models for machine translation: lexicon model,
alignment model, language model training process, generation
& search |
|
Week 12 |
Student’s Presentations |
|