What is a Gaussian HMM?
The Gaussian hidden Markov model (Gaussian HMM) is a type of finite-state-space and homogeneous HMM where the observation probability distribution is the normal distribution, where and are mean and covariance parameters at state .
What is GMM HMM model?
GMM is a probabilistic model which can model N sub population normally distributed. Each component in GMM is a Gaussian distribution. HMM is a statistical Markov model with hidden states. When the data is continuous, each hidden state is modeled as Gaussian distribution.
What are transition and emission probabilities?
The transition probabilities represent the probability of a specific state transition occurring within the HMM for a specific pixel. The emission probabilities refer to the relationship between the hidden state in the model and the observations as provided by the input data.
What is a Gaussian mixture model in a HMM?
Traditionally, the observation probabilities in an HMM, bj ( ot ), are modeled using a Gaussian mixture model. In this case, the model contains the means and covariance matrices of each feature vector for specifying a single Gaussian distribution, and weights for determining the relative contribution of several Gaussians to the final model.
What is the observation probability in machine learning?
For each state, the observation probability is the probability of observing the input feature vector given the state. There are two principal techniques for estimating the observation probabilities, Gaussian mixtures and neural networks.
What is an HMM/Ann hybrid?
When an ANN is used to estimate the observation probabilities, the resulting system is referred to as an HMM/ANN hybrid. The use of ANNs in estimating the observation probabilities yields several benefits, including fast execution time and discriminative training.
What is the difference between transition probabilities and observation probabilities?
While estimation of the transition probabilities is straightforward, estimation of the observation probabilities is less so since they are represented as a multidimensional space. For each state, the observation probability is the probability of observing the input feature vector given the state.