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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. The hidden states form a Markov chain, and the probability distribution of the observed symbol depends on the underlying state.
How Hidden Markov model is used in face recognition?
For face detection, a set of face images is used in the training of one HMM. The images in the training set represent frontal faces of different people taken under different illumination conditions. For face recognition, each individual in the database is represented by an HMM face model.
What is the use of hidden Markov model Mcq?
Explanation: Hidden Markov model is used for solving temporal probabilistic reasoning that was independent of transition and sensor model.
What is used in backward chaining algorithm?
It’s a goal-driven method of reasoning. The endpoint (goal) is subdivided into sub-goals to prove the truth of facts. A backward chaining algorithm is employed in inference engines, game theories, and complex database systems. The modus ponens inference rule is used as the basis for the backward chaining process.
What is machine learning in Mcq?
Explanation: Machine learning is the autonomous acquisition of knowledge through the use of computer programs.
What are things which a Markov decision model have?
In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker.
What are the applications of hidden Markov model?
Hidden Markov Models are widely used in fields where the hidden variables control the observable variables. Speech recognition, Image Recognition, Gesture Recognition, Handwriting Recognition, Parts of Speech Tagging, Time series analysis are some of the Hidden Markov Model applications. 1. Speaker Dependent 2. Speaker Independent 3.
What are HMMs used for in speech recognition?
During recognition, these HMMs provide us an estimate (via probability score) if given sequence of speech segments matches a string of phonemes. Since a string of phonemes can be mapped to a word, HMMs can used to find the most probable word for speech signal.
Terminology in HMM The term hidden refers to the first order Markov process behind the observation. Observation refers to the data we know and can observe. Markov process is shown by the interaction between “Rainy” and “Sunny” in the below diagram and each of these are HIDDEN STATES.
In Speech Recognition, Hidden States are Phonemes, whereas the observed states are speech or audio signal. Hidden Markov Models are widely used in fields where the hidden variables control the observable variables.