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Syllabus
Lectures
Introduction:
01: Organization
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Speech Signals:
02: Production
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03: Digital Models
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04: Perception
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05: Masking
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06: Phonetics and Phonology
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07: Syntax and Semantics
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Signal Processing:
08: Sampling
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09: Resampling
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10: Acoustic Transducers
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11: Temporal Analysis
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12: Frequency Domain Analysis
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13: Cepstral Analysis
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14: Exam No. 1
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15: Linear Prediction
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16: LP-Based Representations
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17: Spectral Normalization
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Parameterization:
18: Differentiation
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19: Principal Components
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20: Linear Discriminant Analysis
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Acoustic Modeling:
21: Dynamic Programming
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22: Markov Models
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23: Parameter Estimation
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24: HMM Training
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25: Continuous Mixtures
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26: Practical Issues
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27: Decision Trees
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28: Limitations of HMMs
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Language Modeling:
29: Formal Language Theory
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30: Context-Free Grammars
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31: Exam No. 2
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32: N-Gram Models and Complexity
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33: Smoothing
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Search:
34: Basic Search Algorithms
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35: Time Synchronous Search
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36: Stack Decoding
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37: Lexical Trees
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38: Efficient Trees
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Miscellaneous Topics:
39: Adaption
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40: Exam No. 3
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41: Discriminative Training
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42: Neural Networks
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Summary:
43: Scoring and Evaluation
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44: Common Evaluation Tasks
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45: State Of The Art
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46: Final Exam
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