Viterbi Algorithm

The Viterbi algorithm is a dynamic programming approach used to find the most likely sequence of hidden states in a Hidden Markov Model (HMM), crucial for tasks like speech recognition and bioinformatics. Current research focuses on improving its efficiency for large-scale applications, such as federated learning and non-autoregressive machine translation, often incorporating techniques like Bayesian inference and adaptive search strategies to reduce computational complexity. These advancements enhance the algorithm's applicability to diverse fields, improving accuracy and speed in tasks ranging from sentiment analysis to target localization.