Novel feature fusion method for speech emotion recognition based on multiple kernel learning

In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. Based on the global features, the local information of different kinds of features is utilized. Both the global and the local features are combined together. Moreover, the multiple kernel learning method is adopted. The global features and each kind of local feature are respectively associated with a kernel, and all these kernels are added together with different weights to obtain a mixed kernel for nonlinear mapping. In the reproducing kernel Hilbert space, different kinds of emotional features can be easily classified. In the experiments, the popular Berlin dataset is used, and the optimal parameters of the global and the local kernels are determined by cross-validation. After computing using multiple kernel learning, the weights of all the kernels are obtained, which shows that the formant and intensity features play a key role in speech emotion recognition. The classification results show that the recognition rate is 78. 74% by using the global kernel, and it is 81.10% by using the proposed method, which demonstrates the effectiveness of the proposed method.

乳液PCR扩增复杂基因序列的方法建立及普通PCR的比较

该研究分析普通PCR的不足,通过较多实验研究初步建立乳液PCR方法,改善PCR实验技术.采用人工合成的乳酸杆菌质粒为模板,不同成分配比条件下的乳液PCR和普通PCR对质粒序列进行扩增,对扩增产物进行琼脂糖凝胶电泳,电泳结束后对凝胶进行Gelred染色并用GS-800灰度扫描仪成像.进一步分析乳液PCR和普通PCR扩增产物的特异性以及不同成分配比条件下乳液PCR的扩增质量.通过比较两种PCR方法的扩增结果可得,在相同条件下,乳液PCR扩增特异性高于普通PCR扩增,并确定当水相中加入100 g/L BSA,水油体积比在1∶2.5时,破乳水饱和乙醚体积比为1∶1时,水油相在1700 rpm条件下连续混合5 min时扩增效果最好.

高等教育现代化背景下专业博士培养路径分析——以医学专业博士为例

医学专业博士教育作为我国高等医学教育体系中的最高层次,代表着我国高精尖医学人才培养的水平。随着高等教育现代化进程的加快和医学技术的进步,现有的医学博士培养模式是否合理直接关系到医学尖端人才培养的质量。美国医学教育处于世界一流水平,本文以斯坦福大学医学院为例,对其办学理念、培养目标、入学条件、课程设置等方面进行探究,从中汲取其有益经验,为高等教育现代化背景下我国医学博士培养的模式提供参考。