| 05 |
08/27 |
2.1.3.4 |
Perception and Masking |
| 06 |
08/30 |
2.2 |
Phonetics and Phonology |
| 07 |
09/01 |
2.3 - 2.5 |
Syntax and Semantics |
| 08 |
09/03 |
5.5, 9.3 |
Sampling |
| 09 |
09/08 |
5.6, 5.7 |
Resampling |
| 10 |
09/10 |
10.1 - 10.4 |
Acoustic Transducers |
| 11 |
09/13 |
5.4 |
Temporal Analysis |
| 12 |
09/15 |
5.1 - 5.3 |
Frequency Domain Analysis |
| 13 |
09/17 |
Lectures 1-11 |
Exam No. 1 |
|
| 14 |
09/20 |
6.4 - 6.5 |
Cepstral Analysis |
| 15 |
09/22 |
6.1 - 6.3 |
Linear Prediction |
| 16 |
09/24 |
6.5.3 |
LP-Based Representations |
| 17 |
09/27 |
6.5.3, 9.3.4 |
Spectral Normalization |
| 18 |
09/29 |
9.3.3 |
Differentiation |
| 19 |
10/01 |
9.3.4, 3.2.2 |
Principal Components |
| 10/04 |
02/22 |
9.3.4, 3.2.2 |
Linear Discriminant Analysis |
| 21 |
10/06 |
8.2.1 |
Dynamic Programming |
| 22 |
10/08 |
8.2.2, 8.2.3 |
Fundamentals of Markov Models |
| 23 |
10/11 |
8.2.4, 4.4.2 |
Parameter Estimation |
| 24 |
10/13 |
8.2.4 |
HMM Training |
| 25 |
10/15 |
4.4.3, 8.3 |
Continuous Mixture Densities |
| 26 |
10/20 |
8.4 |
Practical Issues |
| 27 |
10/22 |
4.5 |
Decision Trees |
| 28 |
10/25 |
Lectures 12 - 28 |
Exam No. 2 |
| 29 |
10/27 |
8.5 |
Limitations of HMMs |
| 30 |
10/29 |
11.1 |
Formal Language Theory |
| 31 |
11/01 |
11.2.1 |
Context Free Grammars |
| 32 |
11/03 |
11.2.2, 11.3 |
N-gram Models and Complexity |
| 33 |
11/05 |
11.4 |
Smoothing |
| 34 |
11/08 |
12.1 |
Basic Search Algorithms |
| 35 |
11/10 |
12.2 - 12.4 |
Time Synchronous Search |
| 36 |
11/12 |
12.5 - 13.6 |
Stack Decoding |
| 37 |
11/15 |
13.1.1 - 13.1.3 |
Lexical Trees |
| 38 |
11/17 |
13.1.4 - 13.1.6 |
Efficent Trees |
| 39 |
11/19 |
3.2.3, 9.6 |
Adaptation |
| 40 |
11/22 |
Lectures 29 - 37 |
Exam No. 3 |
| 41 |
11/29 |
4.3 |
Discriminative Training |
| 42 |
12/01 |
4.3.3, 9.8.1 |
Neural Networks |
| 43 |
12/03 |
N/A |
Evaluation Metrics and Common Evaluations |
| 46 |
12/07 |
Cumulative |
Final Exam (12 - 3 PM) |
Homework: