Surface electromyogram （EMG） signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers＇ right forearm flexor respectively in the process of forearm supination （FS） and forearm pronation （FP）.After the raw action surface EMG （ASEMG） signal was decomposed into several sub-signals with wavelet packet transform （WPT）,five fractal dimensions were respectively calculated from the raw signal and four sub-signals by the method based on fuzzy self-similarity.The results show that calculated from the sub-signal in the band 0 to 125 Hz,the fractal dimensions of FS ASEMG signals and FP ASEMG signals distributed in two different regions,and its error rate based on Bayes decision was no more than 2.26%.Therefore,the fractal dimension is an appropriate feature by which an FS ASEMG signal is distinguished from an FP ASEMG signal.
F0 (fundamental frequency) contour was studied under different prosodic environment in continuous speech and a novel model of F0 contours prediction was proposed. It describes syllabic F0 contour with two points, one curve and duration. The curve represents two optimal points of controlling parameters. The duration represents the syllabic duration. The prosodic characters of controlling parameters were analyzed by CART (Class and Regression Tree). A set of cont rolling parameters was analyzed, which reflects the linguistic environment and prosodic structure. Then it sets up the model of F0 contours prediction with the two optimal controlling parameters and F0 templates. The end pitch value of previous syllable as special prosodic parameters was used to keep the continuity of fore-and-aft syllable. It focuses on looking out the main prosodic clues hiding in F0 contours and applying it to simplify the model for prediction. The results of s ynthesis experiment show that the performance of the prediction method is apprec iated.
