# hw4 - Arthur Kunkle ECE 5526 HW#4 Problem 1 Each HMM was...

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Arthur Kunkle ECE 5526 HW #4

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Problem 1 Each HMM was used to generate and visualize a sample sequence, X. These are the outputs from each HMM. HMM1 HMM2 HMM3 HMM4
HMM5 HMM6 Questions: 1. The following characterize a correct transition matrix: a. Has dimensions of the amount of states b. First column is all 0 (initial state cannot be transitioned to) c. Last row in all 0 except final entry (probability is 1 to enter final state) d. All entries in a row or column (except first column) sum to 1 2. The transition matrix will effect the “duration” of emiisions within particular classes or groups of classes. In the above output visualizations, especially in HMM’s 4-6, the sample chains tend to occur in class clumps. 3. Without a final state, the observation sequence length would be unbounded. 4. A single HMM is specified by: a. D-dimensional mean vector for each state b. DxD-dimensional variance matrix for each state c. NxN-dimensional transition matrix for all transitions d. N-dimensional initial state probability vector Total parameters: D + N + D^2 + N^2 5. A word would use a left-right model. The sequence of phones would be fixed, with a probability of repeating the same phone or longer utterances, which is also supported by this model type. More Questions: 1. log(a+b) = log(a) + log(1 + e^(log(b) – log(a)) = log(a * (1 + e^(log(b) – log(a)) = log(a + a*e^(log(b/a)) = log(a + a * (b/a))

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= log(a + b) If log(a) > log(b), the second implementation would be better suited. This is because the difference in the exponential would yield a result that is much less likely to be asymtotic to zero (where the derivative becomes much sharper). ln(x) 2. log(alpha_t(j)) = log(b_j(x_t)) + log(sum(alpha_t(i) * a_ij)) = log(b_j(x_t)) + sum(log(alpha_t(i) * a_ij)) = log(b_j(x_t)) + sum(log(alpha_t(i)) + log(a_ij)) The biggest performance gain for this converstion is the ability to perform repeated additions instead of multiplications. Because the amount of state transitions can be very large for some HMM’s, this is a critical gain.
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## This note was uploaded on 02/11/2012 for the course ECE 5526 taught by Professor Staff during the Summer '09 term at FIT.

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hw4 - Arthur Kunkle ECE 5526 HW#4 Problem 1 Each HMM was...

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