An Analysis of Hamptonese Using Hidden Markov Models

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An Analysis of Hamptonese Using Hidden Markov
Models

• Ethan Le and Mark Stamp
• Department of Computer Science
• San Jose State University
• McNair Scholars Program
• Summer Research Project, 2003

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Agenda

• James Hampton
• Purpose
• What is Hamptonese?
• Hidden Markov Models (HMMs)
• HMMs and Hamptonese
• Other approach
• Results
• Questions

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James Hampton

• WWII veteran
• Janitor in Washington D.C.
• Life of solitude
• Died in 1964
• Hamptonese discovered after his death
• The Throne also found

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The Throne of the Third Heaven of the Nations
Millennium General Assembly
James Hampton alongside his Throne
Pieces of The Throne
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Purpose

• Analyzed Hamptonese using Hidden Markov Model to
  determine whether Hamptonese was created using
  simple substitution of English letters (or other
  languages).
• Consider alternative interpretations of Hamptonese

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What is Hamptonese?
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What is Hamptonese? (Contd)

• Unknown origin and content
• Spans 167 pages
• Could it be
• A cipher system?
• Gibberish?
• Example of human randomness?
• Other?

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Hidden Markov Models (HMMs)

• Markov process with hidden states
• HMMs provide probabilistic information about the
  underlying state of a model, given a set of
  observations of the system.

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HMMs (Contd)

• Three solvable problems
• Find probability of observed sequence
• Find optimal state sequence
• Train the model to fit observations

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HMMs examples

• Example 1 Music Information Retrieval System
  (MIR)
• Example 2 Deciphering English text (Cave and
  Neuwirth)
• 27 symbols (alphabet letters plus space)
• Assume 2 hidden states
• Train HMM to best fit input data
• Results separation of consonants and vowels

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HMM Example (Contd) English Text
Character Initial Final
A 0.03685 0.03793 0.0044447 0.1306242
B 0.04007 0.03978 0.0241154 0.0000000
C 0.03362 0.03423 0.0522168 0.0000000
D 0.03777 0.03654 0.0714247 0.0003260
E 0.03409 0.03608 0.0000000 0.2105809
F 0.03685 0.03932 0.0374685 0.0000000
G 0.03593 0.03839 0.0296958 0.0000000
H 0.03961 0.03932 0.0670510 0.0085455
I 0.04007 0.03377 0.0000000 0.1216511
J 0.03501 0.03515 0.0065769 0.0000000
K 0.03685 0.03700 0.0067762 0.0000000
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HMMs and Hamptonese

• Transcribed 103 pages
• About 30,000 observations
• More than 40 distinct symbols
• Assume 2 hidden states
• 3 different reading techniques

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Other Approach

• Hamptonese vs. 246 different languages
• HMM for other languages
• Religious references organizational patterns
• Analysis of first 40 pages
• Pattern recognition

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Results

• Hamptonese is not a simple substitution for
  English letters
• Hamptonese is probably not a simple substitution
  for any other language
• First comprehensive study of Hamptonese
• Transcription of entire text
• Future research suggestions

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Acknowledgments

• Dr. Mark Stamp
• SJSU McNair Scholars Program
• SCCUR
• UC Irvine

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Questions???
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References

• Chai, Wei and Vercoe, Barry. (n.d.). Folk Music
  Classification Using Hidden Markov Models. MIT
  Media Lab.
• M. Stamp, A revealing introduction to Hidden
  Markov Modelshttp//www.cs.sjsu.edu/faculty/stamp
  /Hampton/HMM.pdf
• M. Stamp and E. Le, Hamptonese website.http//www
  .cs.sjsu.edu/faculty/stamp/Hampton/hampton.html
• R.L. Cave and L.P. Neuwirth, Hidden Markov Models
  for English, in Hidden Markov Models for Speech,
  IDA-CRD, Princeton, NJ, 1980