ID 原文 译文
58668 首先,通过卷积神经网络和独立循环神经网络获得医疗健康问句的局部语义表示和全部结构表示; We first obtain the questions’ local semantic representation and global structural representation by the convolutional neural network (CNN) and independent recurrent neural network (IndRNN).
58669 然后,利用自注意力机制将得到的局部语义表示和全局结构表示向量融合,生成医疗健康问句的最终语义表示向量; Then, we extract the questions’ semantic representation, and especially we get the questions’ semantic representation by fusing the local semantic representation and global structural representation using a self-attention mechanism.
58670 最后,通过分类层分类并输出分类结果。 Finally, we classify the semantic representation of the medical health question through the softmax layer and output classification result.
58671 实验结果表明,这种方法可以有效地提高模型的语义表示能力,且有效地改善了梯度消失和梯度爆炸问题,在中文医疗健康问句数据集上具有很好的性能。 Experimental results show that this method leads to a good performance in the Chinese medical health questions dataset, and that it effectively improves the semantic representation ability of the model and significantly resolves the gradient vanishing and gradient explosion problems.
58672 针对移动机器人在车间实际工作环境中,由于噪声的影响导致对语音控制命令识别性能差的问题,提出了一种基于伽玛通倒谱系数和Teager能量算子混合特征提取的新算法。 In the real working environment,the mobile robots have a poor recognition performance to speech control commands due to the noise effect. Aiming at this issue,this paper proposes a new algorithm based on the gammatone frequency cepstral coefficient and the mixed feature extraction of the Teager energy operator.
58673 该算法用伽玛通滤波器代替抗噪性比较普通的梅尔滤波器,在提取伽玛通倒谱系数的过程中加入反映语音信号能量的Teager能量算子组成新的特征,并考虑语音信号的动态特性,将其与一阶差分参数融合组成混合特征; This algorithm replaces the common Mel filter with the Gammatone filter. In the process of extracting gammatone frequency cepstral coefficients,the Teager energy operator reflecting the energy of speech signal is added to form a new feature, with the dynamic characteristics of the speech signal considered. It is combined with the first-order difference parameters to form a mixed feature.
58674 应用主成分分析法降维,将得到的混合特征用于移动机器人控制命令的语音识别系统。 And the principal component analysis is made to reduce the dimension,and the final mixed features are used to the speech recognition system for control command of the mobile robot.
58675 实验结果表明,在车间噪声以及信噪比为10dB的环境下,混合特征的识别率较梅尔倒谱系数提高了12.20%,通过主成分分析法得到的混合特征的识别率提高了1. Experimental results show that,in the environment of the workshop noise and signal-to-noise ratio of 10dB,the recognition rate of mixed features is improved by 12. 20% compared with the mel frequency cepstrum coefficient.
58676 02%。 The recognition rate of the mixed feature is increased by 1. 02% when the dimension is reduced by principal component analysis.
58677 为了解决现有的基于人体形变模型的姿态估计算法容易出现误差、组成的运动序列不连贯等问题,提出利用深度相机获取的视频数据、点云数据进行优化的方法。 An optimization algorithm is proposed utilizing the video data and point cloud data captured by the depth camera to solve the problems such as error-proneness and incoherence of motion sequence caused by the existing human pose estimation algorithms based on the morphable model.