Continuous Density HMM

A Continuous Density Hidden Markov Model (CD-HMM) is a statistical tool used to recognize patterns in sequential data, such as speech or gestures. Unlike models that categorize data into fixed groups, CD-HMMs assume that the data points are drawn from smooth, continuous probability distributions, often Gaussian (bell-shaped) curves. This allows the model to effectively handle the natural variability in real-world signals. In essence, CD-HMMs analyze sequences by estimating how likely each segment is to have been generated by specific patterns, enabling accurate recognition even when inputs vary in subtle ways.