Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behaviour. They are used in almost all current speech recognition systems.
The Hidden Markov Model (HMM) is a statistical model used in various fields including speech recognition, natural language processing, bioinformatics, and finance, among others. Here’s a concise and informative answer you can provide in an AI interview:
“A Hidden Markov Model (HMM) is a probabilistic model used for modeling sequential data where underlying states are not directly observable but can be inferred from observed data. It’s particularly useful in tasks involving temporal or sequential data, such as speech recognition, where words are sequences of phonemes, or in natural language processing for part-of-speech tagging and named entity recognition. HMMs are also applied in bioinformatics for gene prediction and sequence alignment, and in finance for modeling stock prices and market behaviors, among other applications.”