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 bManced 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 ridgetiness,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 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 experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition. 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 bManced the search efficiency and population diversity effectively. Secondarily, 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 ridgetiness, 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 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 t he 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 neural network method. The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%, which had improved 1.7%, 2.7%, 3.4%, 4.7% and 7.8% respectively. recognition of fidgetiness was significantly improve, tried verifying that Im-SFLA was an effective SVM parameter selection method, and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.