From the course: Fundamentals of Dynamic Programming

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More applications of Hidden Markov Models

More applications of Hidden Markov Models - Python Tutorial

From the course: Fundamentals of Dynamic Programming

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More applications of Hidden Markov Models

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- Let's look at some different ways hidden Markov models can be applied to real world applications. The first one is speech recognition. Now, sound waves are pretty vague as a concept, so to produce actual observations, we need to first process the audio inputs. What we'll do is take a 25 millisecond slice of the entire sound, pass it through some filters like the discreet cosine transform, and acoustic modeling, to produce a 10-dimensional vector of numbers. We do this every 10 milliseconds producing our observations. As you might expect, the hidden states are syllables, though we also include things like silence and end of fragment. The next application is in DNA sequence analysis. DNA sequences are composed of four components, or nucleotides. A, C, G, and T. A group of three nucleotides in a row form an amino acid, the building block of a protein, but not all nucleotides are actually part of a protein, as some are…

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