应用改进混合蛙跳算法的实用语音情感识别

Recognition of practical speech emotion using improved shuffled frog leaping algorithm

针对支持向量机(Support Vector Machine,SVM)的参数优化问题,提出了一种改进的混合蛙跳算法(Improved Shuffled Frog Leaping Algorithm,Im-SFLA),提高了其在实用语音情感识别中的学习能力。首先,我们在SFLA中引入了模拟退火(Simulated Annealing,SA)、免疫接种(Immune Vaccination,IV)、高斯变异和混沌扰动算子,平衡了搜索的高效性和种群的多样性;第二,利用Im-SFLA优化SVM的参数,提出了一种Im-SFLA-SVM方法;第三,分析了烦躁等实用语音情感的声学特征,重点分析了基音、短时能量、共振峰和混沌特征随情感类别的变化特性,构建出144维的情感特征向量并采用LDA降维到4维;最后,在实用语音情感数据库上测试了算法性能,将提出的算法与混合蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)优化SVM参数的方法(SFLA-SVM方法)、粒子群优化(Particle Swarm Optimization,PSO)算法优化SVM参数的方法(PSO-SVM方法)、基本SVM方法、高斯混合模型(Gaussian Mixture Model,GMM)方法和反向传播(Back Propagation,BP)神经网络法等进行对比。实验结果表明,采用Im-SFLA-SVM方法的平均识别率达到77.8%,分别高于SFLA-SVM方法、PSO-SVM方法、SVM方法、GMM方法和BP神经网络法各1.7%,2.7%,3.4%,4.7%,7.8%,并且对于烦躁这种实用情感的识别率提高效果最为明显,从而证实了Im-SFLA是一种有效的SVM参数选择方法,并且Im-SFLA-SVM方法能显著提升实用语音情感的识别率。

Due to the drawbacks in Support Vector Machine （SVM） parameter optimization, an Improved Shuffled Frog Leaping Algorithm （Im-SFLA） was proposed, and the learning ability in practical speech emotion recognition was improved. Firstly, we introduced Simulated Annealing （SA）, Immune Vaccination （IV）, Gaussian mutation and chaotic disturbance into the basic SFLA, which balanced the search efficiency and population diversity effectively. Secondly, Im-SFLA was applied to the optimization of SVM parameters, and an Im-SFLA-SVM method was proposed. Thirdly, the acoustic features of practical speech emotion, such as fidgetiness, were analyzed. The pitch frequency, short-term energy, formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories, and we constructed a 144-dimensional emotion feature vector for recognition and it was reduced to 4-dimension by adopting Linear Discriminant Analysis （LDA）. Finally, the Im-SFLA-SVM method was tested on the practical speech emotion database, and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM （SFLA- SVM） method, Particle Swarm Optimization algorithm optimization-SVM （PSO-SVM） method, basic SVM, Gaussian Mixture Model （GMM） method and Back Propagation （BP） neural network method. The experimental results showed that the average recognition rate and 7.8% respectively, compared thus verifying that Im-SFLA was significantly improve the practical of Im-SFLA-SVM method was 77.8%, which had improved 1.7%, 2 with the other methods. The recognition of fidgetiness was signific 7%, 3.4%, 4.7% antly improved, an effective SVM parameter selection method, and the Im-SFLA-SVM method may speech emotion recognition