What are Hidden Markov Models (HMMs) used for?

Hidden Markov Models (HMMs) are probabilistic models used to analyze sequential data where the underlying system has hidden (unobservable) states that produce observable outputs. They are particularly effective for tasks where the sequence of events matters, and the true state of the system must be inferred indirectly. Common applications include speech recognition, natural language processing (NLP), bioinformatics, and time-series prediction. HMMs work by modeling transitions between hidden states and the likelihood of observable events occurring from those states, making them suitable for problems with temporal or sequential dependencies.

A key use case for HMMs is in speech recognition. Here, the hidden states represent linguistic units like phonemes or words, while the observations are acoustic signals (e.g., audio waveforms). By training an HMM on labeled speech data, the model learns the probabilities of transitioning between phonemes and the likelihood of specific sounds being associated with each phoneme. Similarly, in NLP, HMMs are applied to tasks like part-of-speech tagging, where hidden states correspond to grammatical categories (nouns, verbs, etc.), and observed data are the words in a sentence. The model predicts the most likely sequence of tags given the word sequence using algorithms like the Viterbi algorithm, which efficiently computes the optimal state path.

Another important application is in bioinformatics, where HMMs identify patterns in DNA or protein sequences. For example, gene-finding algorithms use HMMs to distinguish coding regions (exons) from non-coding regions (introns) by modeling nucleotide sequences as observations and biological states as hidden variables. HMMs are also used in finance for modeling stock price movements or economic regimes, where hidden states represent market conditions (bullish, bearish) and observations are price changes. Developers can implement HMMs using libraries like Python’s hmmlearn or custom code, often leveraging dynamic programming for efficient inference. While newer techniques like deep learning have expanded sequential data modeling, HMMs remain a lightweight, interpretable option for problems with clear state-based structure.