====== Course Outline ======

The course outline is a guideline to topics that will be discussed in the course, and when they will be discussed:

===== Week 1 =====

**Introduction**: application background; a big picture; speech sounds; spoken language
(reading assignment)

===== Week 2 =====

**Math Background**: probabilities; Bayes theorem; statistics; estimation; regression; hypothesis testing; entropy; mutual information; decision tree; optimization theory and convex optimization

===== Week 3 =====

**Pattern Classification**: pattern classification & pattern verification; Bayesian decision theory

===== Week 4 =====

**Generative Models**: model estimation: maximum likelihood, Bayesian learning, EM algorithm; multivariate Gaussian, Gaussian mixture model, Multinomial, Markov Chain model, etc.

===== Week 5 =====

**Discriminative Models**: Linear discriminant functions; support vector machine (SVM); large margin classifiers; sparse kernel machines; Neural networks

===== Week 6 =====

**Hidden Markov Model (HMM)**: HMM vs. Markov chains; HMM concepts; Three algorithms: forward-backward; Viterbi decoding; Baum-Welch learning. 
 
===== Week 7  =====

midterm presentation

===== Week 8 =====

**Automatic Speech Recognition (ASR) (I)**: Acoustic and Language Modeling: 
HMM for ASR;  ASR as an example of pattern classification;
Acoustic modeling: HMM learning (ML, MAP, DT); parameter tying (decision tree based state tying); n-gram models: smoothing, learning, perplexity, class-based.

===== Week 9 =====

**Automatic Speech Recognition (ASR) (II)**: Search - why search; Viterbi decoding in a large HMM; beam search; tree-based lexicon; dynamic decoding; static decoding; weighted finite state transducer (WFST)

===== Week 10 =====

**Spoken Language Processing (I)**: text categorization
classify text documents: call/email routing, topic detection, etc.
vector-based approach, Naïve 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 presentation