Gaussian Mixture Model HMM

Gaussian Mixture Model Hidden Markov Models (GMM-HMMs) are statistical models used to represent sequential data, particularly in speech recognition and financial time series analysis. Current research focuses on improving their performance and applicability through techniques like incorporating neural networks (e.g., combining GMM-HMMs with deep learning architectures or using neural networks to improve data selection for training), and exploring alternative model architectures offering greater expressiveness than traditional GMM-HMMs. These advancements aim to enhance accuracy and efficiency in various applications, including improved speech recognition systems and more robust financial forecasting models. The ongoing development of more efficient and accurate GMM-HMM based methods is crucial for advancing these fields.