The Forward algorithm is a dynamic programming approach used in Hidden Markov Models (HMMs) to calculate the probability of a sequence of observed events. It efficiently computes this probability by iteratively summing over all possible hidden states, making it crucial for tasks like speech recognition and bioinformatics sequence analysis.

Forward Algorithm

Dynamic programming is an optimization strategy used to solve complex problems by breaking them down into simpler subproblems, storing the results of these subproblems to avoid redundant computations. It is particularly effective for problems exhibiting overlapping subproblems and optimal substructure properties, such as the Fibonacci sequence or the shortest path in a graph.

Dynamic Programming

Hidden Markov Models (HMMs) are statistical models that represent systems with unobservable (hidden) states through observable events, using probabilities to model transitions between these states. They are widely used in temporal pattern recognition, such as speech, handwriting, gesture recognition, and bioinformatics, due to their ability to handle sequences of data and uncover hidden structures.

Hidden Markov Models

HMMs

Probability of a sequence

Observed events

Hidden states

Speech recognition is the technology that enables the conversion of spoken language into text by using algorithms and machine learning models. It is crucial for applications like virtual assistants, transcription services, and accessibility tools, enhancing user experience by allowing hands-free operation and interaction with devices.

Concepts

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