Objective To improve the accuracy of speech segmentation through the improved short time fractal dimension. Methods An equation was established for window size selection of speech analysis. Dynamic Window Step (DWS...Objective To improve the accuracy of speech segmentation through the improved short time fractal dimension. Methods An equation was established for window size selection of speech analysis. Dynamic Window Step (DWS), a novel method to determine the sliding window steps adaptively in agreement with the local properties of signals, was proposed. Results The influence of the window step on the short time fractal dimension was discussed. Compared with fixed window steps, more accurate and efficient fractal dimension trajectories were obtained with dynamic window steps. Conclusion The proposed method was applied to a number of speech signals. It shows promise in speech segmentation, speech recognition and other transient signal analysis.展开更多
文摘Objective To improve the accuracy of speech segmentation through the improved short time fractal dimension. Methods An equation was established for window size selection of speech analysis. Dynamic Window Step (DWS), a novel method to determine the sliding window steps adaptively in agreement with the local properties of signals, was proposed. Results The influence of the window step on the short time fractal dimension was discussed. Compared with fixed window steps, more accurate and efficient fractal dimension trajectories were obtained with dynamic window steps. Conclusion The proposed method was applied to a number of speech signals. It shows promise in speech segmentation, speech recognition and other transient signal analysis.
