An Analysis of Hamptonese Using Hidden Markov Models Ethan Le Dr. Mark Stamp Undergraduate Assistant Professor Department of Computer Science Department of Computer Science San Jose State University San Jose State University San Jose, CA, U.S.A. San Jose, CA, U.S.A. Email:
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[email protected] An Analysis of Hamptonese Using Hidden Markov Models Le and Stamp Table of Contents Section Page 1. Introduction 5 of 54 1.1. James Hampton 5 of 54 2. Purpose 7 of 54 3. What is Hamptonese? 8 of 54 3.1. Description of Hamptonese Text 8 of 54 3.2. Transcription 9 of 54 3.3. Frequency Counts 14 of 54 4. Hidden Markov Models (HMMs) 14 of 54 4.1. Hidden Markov Models Applications 15 of 54 4.1.1. HMM in Speech Recognition Algorithms 15 of 54 4.1.2. Music-Information Retrieval and HMMs 16 of 54 4.1.3. English Alphabet Analysis Using HMMs 17 of 54 5. English Text Analysis Using Hidden Markov Models 17 of 54 6. Modeling the Hamptonese HMM 19 of 54 7. Hamptonese Analysis 19 of 54 7.1. Reading Techniques 19 of 54 7.2. HMM Parameters 20 of 54 8. Hamptonese HMM Results 21 of 54 8.1. Non-Grouped 21 of 54 8.2. Grouped 22 of 54 9. English Phonemes 27 of 54 9.1. English Phonemes and Hamptonese 29 of 54 10. Entropy, Redundancy, and Word Representation 29 of 54 10.1. Entropy 30 of 54 10.2. Redundancy 31 of 54 10.3.