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Hidden Markov Models (HMMs): HMMs are statistical models where the system being modeled is assumed to be a Markov process with hidden states. The "hidden" aspect comes from our inability to directly observe the states. Instead, we have access to a set of observable variables that provide some information about the hidden states.
In our case, the observable variables are sound data, and the hidden states represent the underlying process (like phonemes in speech) that generated these sounds.
Here's a breakdown of what I did:
1️⃣ I created random 'sound' data sequences, intended to mimic the variations we encounter in actual speech patterns. This is the kind of data we need when working with Hidden Markov Models in a speech recognition context.
2️⃣ I employed the hmmlearn Python library to train a Gaussian Hidden Markov Model on this sound data. The aim here is to uncover the 'hidden' states that generate the observed sound data - a crucial step in any HMM-based application.
3️⃣ Once the model was trained, it was time for prediction! Using the trained model, I predicted the sequence of 'states' for a new piece of sound data. This mirrors the process of recognizing phonemes in a speech signal, a key step in speech recognition systems.
To make the outcomes more tangible, I employed Matplotlib to visualize the original sound data and the predicted sequence of states. This exercise helped in understanding and demonstrating the application of HMMs

Github: https://lnkd.in/dVkGf69Y

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