To alleviate the problem of the dependency on the quantity of the training sample data in speech emotion recognition, a weighted gradient pre-train algorithm for low-resource speech emotion recognition is proposed. Multiple public emotion corpora are used for pre-training to generate shared hidden layer (SHL) parameters with the generalization ability. The parameters are used to initialize the downsteam network of the recognition task for the low-resource dataset, thereby improving the recognition performance on low-resource emotion corpora. However, the emotion categories are different among the public corpora, and the number of samples varies greatly, which will increase the difficulty of joint training on multiple emotion datasets. To this end, a weighted gradient (WG) algorithm is proposed to enable the shared layer to learn the generalized representation of different datasets without affecting the priority of the emotion recognition on each corpus. Experiments show that the accuracy is improved by using CASIA, IEMOCAP, and eNTERFACE as the known datasets to pre-train the emotion models of GEMEP, and the performance could be improved further by combining WG with gradient reversal layer.

SM3 is a hash function standard defined by China. Unlike SHA-1 and SHA-2, it is hard for SM3 to speed up the throughput because it has more complicated compression function than other hash algorithm. In this paper, we propose a 4-round-in-1 structure to reduce the number of rounds, and a logical simplifying to move 3 adders and 3 XOR gates from critical path to the non-critical path. Based in SMIC 65nm CMOS technology, the throughput of SM3 can achieve 6.54Gbps which is higher than that of the reported designs.