Since the gesture signals in gesture interaction are similar and instable, an acceleration gesture recognition method based on Random Projection (RP) was designed and implemented. The system incorporated two parts, one was the training stage and the other was the testing stage. In the training stage, the system employed Dynamic Time Warping (DTW) and Affinity Propagation (AP) algorithms to create exemplars for each gesture; in the testing stage, the method firstly calculated the distance between the unknown trace and all exemplars to find the candidate traces, then used the RP algorithm to translate all the candidate traces and the unknown trace onto the same lower dimensional subspace, and by formulating the whole recognition problem as an l₁-minimization problem, the unknown trace was recognized. The experimental results on 2400 gesture traces show that the proposed algorithm achieves an accuracy rate of 98.41% for specific individuals and 96.67% for unspecific individuals, and it can effectively identify acceleration gestures.

In order to effectively improve the recognition accuracy of the speech emotion recognition system, an improved speech emotion recognition algorithm based on Support Vector Machine (SVM) was proposed. In the proposed algorithm, the SVM parameters, penalty factor and nuclear function parameter, were optimized with genetic algorithm. Furthermore, an emotion recognition model was established with SVM method. The performance of this algorithm was assessed by computer simulations, and 91.03% and 96.59% recognition rates were achieved respectively in seven-emotion recognition experiments and common five-emotion recognition experiments on the Berlin database. When the Chinese emotional database was used, the rate increased to 97.67%. The obtained results of the simulations demonstrate the validity of the proposed algorithm.