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Unformatted text preview: the BaumWelch algorithm for training a discrete HMM. a Train two fully connected HMMs each with one ∗ hidden node (one HMM for each class of data) and transition probabilities. i Implement the Viterbi algorithm to decode each test sequence using both HMMs. Show the log probability of each test sequence using each HMM. 1 ii Compute the recognition accuracy on the entire test set. iii List the output probabilities and state transition probabilities of each HMM. iv State the threshold you are using and the maximum number of iterations. v Include a complete listing of your source code. b Repeat this problem (a) replacing one ∗ with three . c Repeat this problem (a) replacing one ∗ with fve . 2...
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This note was uploaded on 12/04/2011 for the course ESD 1.124 taught by Professor Kevinamaratunga during the Fall '00 term at MIT.
 Fall '00
 KevinAmaratunga

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