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A Fractal-Based Approach for Speech Segmentation

Nowadays, fractal analysis has been successfully applied to digital speech processing, particularly for word and phoneme segmentation, which represents one of the fundamental steps in automatic speech recognition systems. The practical use of fractal analysis for this purpose should match two principles: low computational cost, to allow the use in real-time, and accuracy in the results, in order to produce a satisfactory segmentation, sending the correct data to the classifier. Aiming at meeting these two requirements, this work proposes a technique for speech segmentation based on the fractal dimension, which is obtained by using the discrete wavelet transform that avoids the use of 1/k pre-filtering. Many families of wavelets are presented and compared, and the results assure the efficacy of the proposed method.