Howtosignalprocessing
- What is HMM used for?
- What is HMM in pattern recognition?
- Why is HMM generative?
- How HMM is used in speech recognition?
What is HMM used for?
A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol' and the invisible factor underlying the observation a `state'.
What is HMM in pattern recognition?
Hidden Markov models (HMMs) are frequently implemented for gesture recognition. From: Encyclopedia of Biomedical Engineering, 2019.
Why is HMM generative?
HMMs are a generative model—that is, they attempt to recreate the original generating process responsible for creating the label-word pairs. As a generative model, HMMs attempt to model the most likely sequence of labels given a sequence of terms by maximizing the joint probability of the terms and labels.
How HMM is used in speech recognition?
The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a given speech model.
