[1]LI Qianqian,SHEN Xiaoyan,REN Fuji,et al.Investigation of multiple speech emotion classification algorithms based on data enhancement[J].CAAI Transactions on Intelligent Systems,2021,16(1):170-177.[doi:10.11992/tis.202103005]
Copy

Investigation of multiple speech emotion classification algorithms based on data enhancement

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 1 Page number: 170-177 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-01-05
Title:
Investigation of multiple speech emotion classification algorithms based on data enhancement
Author(s):
LI Qianqian1; 2;  SHEN Xiaoyan1;  REN Fuji2;  KANG Xin2
1. Institute of Information Science and Technology, Nantong University, Nantong 226019, China;
2. Department of Intelligent Information Engineering, Tokushima University, Tokushima 7708501, Japan
Keywords:
speech emotion recognition; data enhancement; emotion feature; support vector machine; random forest; K-nearest neighbor; low-level description features; machine learning
CLC:
TP181
DOI:
10.11992/tis.202103005
Abstract:
Currently, problems in speech emotion recognition, such as insufficient speech samples and numerous extracted and irrelevant features, make the recognition rate low. To solve the problem of insufficient speech samples, a time-frequency domain data enhancement method is proposed in the preprocessing stage to expand the original database. Considering the current situation where traditional algorithms extract a large amount of feature data and many are emotion-independent, 1582-dimensional emotion features and 10 groups of low-level description features were extracted. Finally, a comparative experiment was performed on three machine learning algorithms: the support vector machine, random forest, and K-nearest neighbor. Experiments showed that the average recognition rate of the support vector machine was superior. Among the ten sets of features, the accuracy of LogMelFreqBand in the three algorithms was 74.63%, 64.93%, and 66.42%, respectively, and the accuracy of pcm_fftMag_mfcc was 84.33%, 73.13%, and 58.21%, respectively.
References:
[1] 吴雪, 宋晓茹, 高嵩, 等. 基于数据增强的卷积神经网络火灾识别[J]. 科学技术与工程, 2020, 20(3):1113-1117.
WU Xue, SONG Xiaoru, GAO Song, et al. Convolution neural network based on data enhancement for fire identification[J]. Science technology and engineering, 2020, 20(3):1113-1117.
[2] CHATZIAGAPI A, PARASKEVOPOULOS G, SGOUROPOULOS D, et al. Data augmentation using GANs for speech emotion recognition[C]//Proceedings of the 20th Annual Conference of the International Speech Communication Association. Graz, Austria, 2019.
[3] ESCUDERO J P, NOVOA J, MAHU R, et al. An improved DNN-based spectral feature mapping that removes noise and reverberation for robust automatic speech recognition[J]. arXiv:1803.09016, 2018.
[4] REN Fuji, MATSUMOTO K. Semi-automatic creation of youth slang corpus and its application to affective computing[J]. IEEE transactions on affective computing, 2016, 7(2):176-189.
[5] KAWASE T, NIWA K, HIOKA Y, et al. Automatic parameter switching of noise reduction for speech recognition[J]. Journal of signal processing, 2017, 21(2):63-71.
[6] YOUSEFI H, KANI A T, KANI I M, et al. Wavelet-based iterative data enhancement for implementation in purification of modal frequency for extremely noisy ambient vibration tests in Shiraz-lran[J]. Frontiers of structural and civil engineering, 2020, 14(2):446-472.
[7] ELBAROUGY R, AKAGI M. Feature selection method for real-time speech emotion recognition[C]//Proceedings of the 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment. Seoul, Korea, 2017:1-6.
[8] MANGALAM K, GUHA T. Learning spontaneity to improve emotion recognition in speech[C]//Proceedings of the 19th Annual Conference of the International Speech Communication Association. Hyderabad, India, 2018.
[9] CUBUK E D, ZOPH B, MANé D, et al. AutoAugment:learning augmentation strategies from data[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA, 2019.
[10] CHEN Mingyi, HE Xuanji, YANG Jing, et al. 3-D convolutional recurrent neural networks with atten-tion model for speech emotion recognition[J]. IEEE signal processing letters, 2018, 25(10):1440-1444.
[11] SCHULLER B W, STEIDL S, BATLINER A, et al. The INTERSPEECH 2010 paralinguistic challenge[C]//Proceedings of the 11th Annual Conference of the International Speech Communication Association. Makuhari, Chiba, Japan, 2010:1342-6230.
[12] BOSER B E, GUYON I M, VAPNIK V N. A training algorithm for optimal margin classifiers[C]//Proceedings of the 5th Annual Workshop on Computational Learning Theory. Pittsburgh, PA, USA, 1992:144-152.
[13] VAPNIK V N. The nature of statistical learning theory[M]. New York:Springer-Verlag, 1995.
[14] VAPNIK V N. An overview of statistical learning the-ory[J]. IEEE transactions on neural networks, 1999, 10(5):988-999.
[15] 戴志诚, 李小年, 陈增照, 等. 基于KNN算法的可变权值室内指纹定位算法[J]. 计算机工程, 2019, 45(6):310-314.DAI Zhicheng, LI Xiaonian, CHEN Zengzhao, et al. Variable-weight indoor fingerprinting localization algorithm based on KNN algorithm[J]. Computer engineering, 2019, 45(6):310-314.
[16] LARIJANI M R, ASLI-ARDEH E A, KOZEGAR E, et al. Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means[J]. Food science & nutrition, 2019, 7(12):3922-3930.
[17] SATHISHKUMAR R, KALAIARASAN K, PRABHAKARAN A, et al. Detection of lung cancer using SVM classifier and KNN algorithm[C]//Proceedings of 2019 IEEE International Conference on System, Computation, Automation and Networking. Pondicherry, India, 2019.
[18] 连天友, 余勤. 改进KNN算法对人体身份的识别[J]. 计算机工程与应用, 2019, 55(11):142-146, 243.LIAN Tianyou, YU Qin. Human identity recognition using improved KNN method[J]. Computer engineering and applications, 2019, 55(11):142-146, 243.
[19] PAUL A, MUKHERJEE D P, DAS P, et al. Improved random forest for classification[J]. IEEE transactions on image processing, 2018, 27(8):4012-4024.
[20] YE?ILKANAT C M. Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm[J]. Chaos, solitons & fractals, 2020, 140:1-8.
[21] BUTT A M, BHATTI Y K, HUSSAIN F. Emotional speech recognition using SMILE features and random forest tree[M]. BI Yaxin, BHATIA R, KAPOOR S. Intelligent Systems and Applications. Cham:Springer, 2020.
[22] DAI Jingzhao, ZHANG Yaan, HOU Jintao, et al. Sparse wavelet decomposition and filter banks with CNN deep learning for speech recognition[C]//Proceedings of 2019 IEEE International Conference on Electro Information Technology. Brookings, SD, USA, 2019.
Similar References:

Memo

-

Last Update: 2021-02-25

Copyright © CAAI Transactions on Intelligent Systems