Improved tree model for arabic speech recognition

This paper introduces a fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal. The experimental results obtained on a spoken Arabic digit dataset confirmed that for the same rate of recognition the proposed method, in terms of time computation is much faster than the state of art algorithm that use the maximum weight spanning tree (MWST).