Hybrid HMM

Hybrid Hidden Markov Models (HMMs) combine the probabilistic framework of HMMs with other techniques, primarily neural networks, to improve modeling of complex sequential data. Current research focuses on enhancing HMM performance through architectural innovations like coupled and factored HMMs, and developing efficient inference algorithms such as particle filtering and variations of the Baum-Welch algorithm to address challenges like high dimensionality and missing data. These advancements are impacting diverse fields, improving accuracy in applications ranging from speech recognition and healthcare time series analysis to RNA folding prediction and named entity recognition.