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HMM Project

# HMM Project - Fundamentals of Speech Recognition Suggested...

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2/9/2005 11:55 AM 1 HMM Special Project Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model 1. Project Introduction For this project, it is proposed that you design and implement a hidden Markov model (HMM) that optimally matches the behavior of a set of training sequences that will be provided to you as part of this project. The goal will be to use the standard set of forward-backward estimation algorithms to optimally determine the best (maximum likelihood) HMM that matches a given set of training data. You are also asked to use the Viterbi algorithm for estimating the model parameters and comparing and contrasting the results for the two methods. You may also want to investigate the effects of using subsets of the training data on the estimated models. Finally, if time permits, you are asked to design your own sequence generator and determine the effects of changing the training sequence characteristics on the estimated models. 2. HMM Generative Models A total of four models were created with the following characteristics: Model 1: number of states, Q=5 type of density of observations: discrete number of observation possibilities, K=5 type of model: ergodic state transition matrix density: random state observation matrix density: random state prior density: random Model 2: number of states, Q=5 type of density of observations: discrete number of observation possibilities, K=5 type of model: ergodic state transition matrix density: skewed state observation matrix density: skewed state prior density: skewed Model 3: number of states, Q=5 type of density of observations: discrete number of observation possibilities, K=5 type of model: left-right state transition matrix density: constrained

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2/9/2005 11:55 AM 2 HMM Special Project state observation matrix density: random state prior density: constrained Model 4: number of states, Q=5 type of density of observations: discrete number of observation possibilities, K=5 type of model: left-right state transition matrix density: constrained state observation matrix density: skewed state prior density: constrained A plot showing the generic structure of both the ergodic model (Figure 1) and the left- right model (Figure 2) is given below. (Note that only a subset of the state transitions are shown in the figure since it got very messy showing all possible state-state transition paths.) Figure 1—5 state ergodic model (only some of the actual state transitions shown in figure) Figure 2—5 state left-right model 3. HMM Training Sequences For this project there are four training sets (available from the course website), labeled: 1. hmm_observations_ergodic_random.mat, 2. hmm_observations_ergodic_skewed.mat,
2/9/2005 11:55 AM 3 HMM Special Project 3. hmm_observations_left-rt_random.mat 4. hmm_observations_left-rt_skewed.mat The characteristics of these four datasets are as follows: 1. each mat file contains the following information: a. an observation array, called data(nex,T), where nex is 50 and T is 100.

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HMM Project - Fundamentals of Speech Recognition Suggested...

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