Graphical Models: Statistical inference vs. determination

Using discrete Hidden-Markov-Models (HMMs) for recognition requires the quantization of the continuous feature vectors. In handwritten whiteboard note recognition it turns out that the pen-pressure information, which is important for recognition, is not adequately quantized and looses significance. In this paper, the implicit modeling of the pressure information presented in previous work which uses the deterministic knowledge on the actual pressure is generalized using a Graphical Model (GM) representation based on statistical inference. The results of two state-of-the-art toolboxes implementing HMMs and GMs are compared. It can be seen that the statistical inference approach based on GMs is inferior to the implicit modeling of the pressure information. It is shown that a direct implementation of HMMs outperforms the mathematic identical GM representation.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Joachim Schenk, Benedikt Hörnler, Artur Braun, Gerhard Rigoll
• Keywords: ICASSP 2009, Implicit Modeling, Signal Processing, Statistical Inference, Whiteboard Note Recognition