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| Basic architecture |
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| of standard speech recognition technology |
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| 1. Bayes’
Rule: P(W|S) ∝ P(S|W)P(W) |
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| 2. Approximate
P(S|W)P(W) |
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| as a Hidden
Markov Model |
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| a probabilistic function
[ to get P(S|W)] |
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| of a markov chain [ to get P(W) ] |
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| 3. Use Baum/Welch
(=EM) algorithm |
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| to “learn” HMM parameters |
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| 4. Use Viterbi
decoding |
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| to find the most probable W given S |
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| in terms of the estimated HMM |
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