PDF estimation

All PDFs were modeled as an HMM with Gaussian state PDFs (single-component Gaussian mixture) in accordance with the method of Rabiner [57]. We used $ M=7$ Markov states for HMM. We used fewer Markov states ($ M=5$) for DAF-HMM. These numbers were chosen by trial and error to provide the best classification performance. It makes intuitive sense also. The additional derivative information inherent in DAF permits modeling dynamic behavior with fewer discrete states. Also, the increased feature dimension of DAF makes it wise to reduce the number of states, or risk over- parameterization. Each PDF was estimated from training data using five trials in which the initial parameters were randomly initialized. The PDF parameters achieving the highest log-likelihood after convergence was chosen.

Baggenstoss 2017-05-19