Explain the Hidden Markov model.

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Bhavesh Badani · Answer

Let’s break down the Hidden Markov Model (HMM) in simple terms: What Is an Hidden Markov Model ? An HMM is a type of graphical model used to analyze temporal data. It’s particularly handy when dealing with sequences of observations over time.Unlike traditional Markov models, HMMs assume that the data we observe isn’t directly the state of the model. Instead, it’s generated by hidden states underlying the system. The Basics: Imagine you’re listening to someone speak. The sounds entering your ears are the observations, but they don’t directly reveal the speaker’s true state (like the shape of their mouth or vocal chord frequency).The HMM assumes that there’s a latent (hidden) process influencing these observations. We can’t directly see this process, but it’s there. How It Works: HMMs evolve based on two rules: State Transitions: The model moves from the current state to the next state (which could be the same state) based on a probability distribution. This transition depends only on the current state.Observations: After each transition, the model emits an observation. The distribution of this observation depends only on the current state. In other words, HMMs allow us to infer a distribution over states at any point in time based on the observed data. Applications: HMMs find use in various fields: Speech Recognition: They help recognize spoken words even when the underlying states (phonemes) aren’t directly observable.Handwriting Recognition: Similar idea—inferring the hidden states behind handwritten characters.Speech Synthesis: Generating speech from text by modeling the underlying phonetic states.

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Explain the Hidden Markov model.

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