| Date | Lecture Topic | Reading |
| 4/7 (T) | Course Overview. Filter-Bank Processing, STFT | 3.2.2.0-6 |
| 4/9 (R) | Spectrogram Reading | 2.4 |
| 4/14 (T) | Linear Prediction, LAR, LSF. LPC Cepstrum. | 3.3, 4.5.7 |
| 4/16 (R) | Spectral, Cepstral, and LPC Distortion Metrics | 4.5.0-4 |
| 4/21 (T) | Auditory Physiology and Psychophysics | 4.4, 4.5.6 |
| 4/23 (R) | Perceptual Frequency Scaling. Noise-Masker Ratio | 4.4, 4.5.6 |
| 4/28 (T) | Scalar and Vector Quantization | 3.4, R\&S 5.3 |
| 4/30 (R) | Predictive Waveform Coding, Perceptual Error Weighting | O'Shau. 7.5-7.7 |
| 5/5 (T) | LPC-Based Analysis-by-Synthesis Coding | Kondoz 6.1-6.2 |
| 5/7 (R) | Recognition using a Hidden Markov Model | 6.2-3, 6.5 |
| 5/12 (T) | Training a Hidden Markov Model | 6.4 |
| 5/14 (R) | Exam Review | (none) |
| 5/19 (T) | Midterm Exam | (none) |
| 5/21 (R) | Hidden Markov: Training, Implementation Issues | 6.4, 6.12 |
| 5/26 (T) | Observation Probabilities: Mixture Gaussian Models, Neural Networks | 6.6, 2.5.4, notes |
| 5/28 (R) | Spectral Dynamics, Delta Cepstrum, RASTA. Explicit Duration Densities | 4.6, 6.9 |
| 6/2 (T) | Connected-Word Recognizers | 7.1-4 |
| 6/4 (R) | Sub-word Units, Context Dependence, and Training Issues | 8.4, 8.9 |
| 6/9 (T) | Language Modeling, Statistical and Semantic. Perplexity. Dialog Systems | 8.5-7, notes |
| 6/11 (R) | Project Presentations and Competition | (none) |