Tree distribution classifier for automatic spoken Arabic digit recognition

In this work we propose a novel method for automatic discrete speech recognition composed from two steps. In a first step, discrete speech features are extracted by means of Mel Frequency Cepstral Coefficients (MFCCs) followed by vector quantization (VQ). Then in a second step, the obtained features are fed to a Tree distribution classifier which provides the class-label associated with each feature by approximating the true class probability by means of an optimal spanning tree model. The experimental results obtained on a spoken Arabic digit dataset confirmed the promising capabilities of the proposed approach.