A face recognition system is a computer application proficient of verifying or identifying a person from a video frame or a digital image from a video source. The human face acts a significant role in the social communication, passing on people’s uniqueness. By means of the human face as a key to protection, biometric face recognition technology has attained noteworthy consideration in the precedent numerous years owing to its prospective for an extensive assortment of applications in both non-law enforcement and law enforcement activities. In this paper, the Speech Emotion Recognition (SER) is analyzed by adopting cepstral features for feature extraction and k-NN classifier for classification. Moreover, the implemented process is compared with k-means and C-means algorithms and the results are obtained.
Shaoling Jing, Xia Mao, Lijiang Chen, “Prominence features: Effective emotional features for speech emotion recognition”, Digital Signal Processing, vol. 72, pp. 216-231, January 2018.
Qirong Mao, Guopeng Xu, Wentao Xue, Jianping Gou, Yongzhao Zhan, “ Learning emotion-discriminative and domain-invariant features for domain adaptation in speech emotion recognition”, Speech Communication, vol. 93, pp. 1-10, October 2017.
Fiacco AV, McCormick GP (1968) Nonlinear programming techniques: sequential unconstrained minimization techniques. Wiley, New York
Qirong Mao, Guopeng Xu, Wentao Xue, Jianping Gou, Yongzhao Zhan, “ Learning emotion-discriminative and domain-invariant features for domain adaptation in speech emotion recognition”, Speech Communication, vol. 93, pp. 1-10, October 2017.
Haytham M. Fayek, Margaret Lech, Lawrence Cavedon, “Evaluating deep learning architectures for Speech Emotion Recognition”, Neural Networks, vol. 92, pp. 60-68, August 2017.
Jiang Xiaoqing, Xia Kewen, Lin Yongliang, Bai Jianchuan, “ Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning”, The Journal of China Universities of Posts and Telecommunications, vol. 24, no. 2, pp. 1-17, April 2017.
Zhen-Tao Liu, Min Wu, Wei-Hua Cao, Jun-Wei Mao, Guan-Zheng Tan, “Speech emotion recognition based on feature selection and extreme learning machine decision tree”, Neurocomputing, vol. 273, pp. 271-280, 17 January 2018.
Sara Motamed, Saeed Setayeshi, Azam Rabiee, “Speech emotion recognition based on a modified brain emotional learning model”, Biologically Inspired Cognitive Architectures, vol. 19, pp. 32-38, January 2017.
Lijiang Chen, Xia Mao, Yuli Xue, Lee Lung Cheng, “Speech emotion recognition: Features and classification models”, Digital Signal Processing, vol. 22, no. 6, pp. 1154-1160, December 2012.
Yogesh C.K., M. Hariharan, Ruzelita Ngadiran, A.H. Adom, Kemal Polat, “Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech”, Applied Soft Computing, vol. 56, pp. 217-232, July 2017.
Moataz El Ayadi, Mohamed S. Kamel, Fakhri Karray, “Survey on speech emotion recognition: Features, classification schemes and databases”, Pattern Recognition, vol. 44, no. 3, pp. 572-587, March 2011.
Jae-Bok Kim, Jeong-Sik Park, “Multistage data selection-based unsupervised speaker adaptation for personalized speech emotion recognition”, Engineering Applications of Artificial Intelligence, vol. 52, pp. 126- 134, June 2016.
Farah Chenchah, Zied Lachiri, “Speech Emotion Recognition in Acted and Spontaneous Context”, Procedia Computer Science, vol. 39, pp. 139-145, 2014.
Siqing Wu, Tiago H. Falk, Wai-Yip Chan, “Automatic speech emotion recognition using modulation spectral features”, Speech Communication, vol. 53, no. 5, pp. 768-785, May–June 2011.
Soroosh Mariooryad, Carlos Busso, “Compensating for speaker or lexical variabilities in speech for emotion recognition”, Speech Communication, vol. 57, pp. 1-12, February 2014.
Edmondo Trentin, Stefan Scherer, Friedhelm Schwenker, “Emotion recognition from speech signals via a probabilistic echo-state network”, Pattern Recognition Letters, vol. 66, pp. 4-12, 15 November 2015.
Leandro D. Vignolo, S.R. Mahadeva Prasanna, Samarendra Dandapat, H. Leonardo Rufiner, Diego H. Milone, “Feature optimisation for stress recognition in speech”, Pattern Recognition Letters, vol. 84, pp. 1-7, 1 December 2016
Rahul B. Lanjewar, Swarup Mathurkar, Nilesh Patel, “Implementation and Comparison of Speech Emotion Recognition System Using Gaussian Mixture Model (GMM) and K- Nearest Neighbor (K-NN) Techniques”, Procedia Computer Science, vol. 49, pp. 50-57, 2015.
B. Yang, M. Lugger, “Emotion recognition from speech signals using new harmony features”, Signal Processing, vol. 90, no. 5, pp. 1415-1423, May 2010.
Khiet P. Truong, David A. van Leeuwen, Franciska M.G. de Jong, “ Speech-based recognition of self-reported and observed emotion in a dimensional space”, Speech Communication, vol. 54, no. 9, pp. 1049-1063, November 2012.
Milton Sarria-Paja, Tiago H. Falk, “Fusion of auditory inspired amplitude modulation spectrum and cepstral features for whispered and normal speech speaker verification”, Computer Speech & Language, vol. 45, pp. 437- 456, September 2017.
Zhenyun Deng, Xiaoshu Zhu, Debo Cheng, Ming Zong, Shichao Zhang, “Efficient kNN classification algorithm for big data”, Neurocomputing, vol. 195, pp. 143-148, 26 June 2016.
S. Borgwardt, A. Brieden, P. Gritzmann,”An LP-based k-means algorithm for balancing weighted point sets”, European Journal of Operational Research, vol. 263, no. 2, pp. 349-355, 1 December 2017
Adrian Stetco, Xiao-Jun Zeng, John Keane, “ Fuzzy C-means++: Fuzzy C-means with effective seeding initialization”, Expert Systems with Applications, vol. 42, no. 21, pp. 7541-7548, 30 November 2015.
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