Hidden Markov Models
Hidden Markov Models (HMMs) are statistical models used to describe the behavior of a system over time, where the system’s internal state is unknown but can be inferred based on observed outputs. HMMs have been widely used in speech recognition, machine translation, bioinformatics, and many other fields.
The basic idea behind HMMs is to model the system as a Markov process, where the state of the system at any given time depends only on its state at the previous time step. The key difference between a regular Markov process and an HMM is that in an HMM, the system’s state is not directly observable. Instead, the system generates outputs (observations) at each time step, and these observations are related to the underlying state of the system in some way.
The HMM is defined by three components: the state transition probabilities, the output probabilities, and the initial state distribution. The state transition probabilities describe the probability of transitioning from one state to another. The output probabilities describe the probability of generating a particular observation given a particular state. The initial state distribution describes the probability distribution over the initial state of the system.
Given these three components, an HMM can be used to compute the probability of a particular sequence of observations, as well as the most likely sequence of states that generated those observations. This is done using the forward-backward algorithm, which is an efficient way to compute the probability of a sequence of observations given the model parameters.
HMMs can be trained using the Baum-Welch algorithm, which is a variant of the expectation-maximization (EM) algorithm. The Baum-Welch algorithm iteratively updates the model parameters to maximize the likelihood of the observed data. This is done by computing the expected sufficient statistics of the model given the observed data, and then updating the model parameters to maximize the expected log-likelihood.
One of the key advantages of HMMs is that they can model complex dependencies between observations and states. For example, in speech recognition, the observed acoustic signal is highly dependent on the underlying phonetic state of the speaker, which in turn is dependent on the previous phonetic state. HMMs can capture these dependencies by modeling the system as a sequence of hidden states that generate the observed acoustic signal.
HMMs have also been extended in various ways to handle more complex scenarios. For example, in the case of continuous observations (e.g., the acoustic signal in speech recognition), HMMs can be extended to Gaussian mixture models (GMMs), where each state is associated with a mixture of Gaussian distributions. In the case of multiple observations streams (e.g., the acoustic and language model features in speech recognition), HMMs can be extended to hidden Markov model networks (HMNets), where each state generates observations from multiple streams.
In conclusion, HMMs are a powerful statistical model for modeling time-series data where the underlying state of the system is unknown but can be inferred based on observed outputs. HMMs have been widely used in various fields, and have been extended in various ways to handle more complex scenarios