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A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

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Abstract

Speech is a unique characteristic of humans that expresses one's emotional viewpoint to others. Speech emotion recognition (SER) identifies the speaker's emotion from the speech signal. Nowadays, (SER) plays a vital role in real-time applications such as human–machine interface, lie detection, virtual reality, security, audio mining, etc. But in SER, filtering the noise content and extracting the emotional features is complex. Moreover, incorporating digital filters increases the cost and complexity of the system. Thus, a novel hybrid firefly-based recurrent neural speech recognition (FbRNSR) was developed with preprocessing and a feature analysis module to classify human emotions based on the speech input. The extracted features from the feature extraction module are trained to classify the emotions as happy, sad, or average. Moreover, the incorporation of firefly fitness improves the classification rate. The presented model is executed in Python, and the results are estimated. The performance of the presented approach is analyzed using the confusion matrix. The designed model achieved high true positive rate of 99.34%, true negative rate of 99.12%, false positive of 99.21%, and false negative rate of 99.07%. The designed model achieved 99.2% accuracy, 98.9% recall, and precision value for the speech signal dataset. Finally, the effectiveness and robustness of the proposed approach are proved by comparing it with the existing techniques. Hence, this method is applicable in various sectors such as medicine, security, etc., to identify the state of emotions among the people.

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Abbreviations

SER:

Speech emotion recognition

NN:

Neural network

ASI:

Automatic speech identification

SVM:

Support vector machine

RNN:

Recurrent neural network

FFA:

Firefly optimization algorithm

FbRNSR:

Firefly-based recurrent neural speech recognition

DWT:

Discrete wavelet transform

References

  1. Zhang, L., Fu, W., Shi, F., Zhou, C., & Liu, Y. (2022). A parallel turbo decoder based on recurrent neural network. Wireless Personal Communications. https://doi.org/10.1007/s11277-022-09779-8

    Article  Google Scholar 

  2. Subhashini, J., & Kumar, C. M. (2019). An algorithm to identify syllable from a visual speech recognition system. Wireless Personal Communications, 107, 2105–2121. https://doi.org/10.1007/s11277-019-06374-2

    Article  Google Scholar 

  3. Sharma, U., Maheshkar, S., Mishra, A. N., & Kaushik, R. (2019). Visual speech recognition using optical flow and hidden Markov model. Wireless Personal Communications, 106, 2129–2147. https://doi.org/10.1007/s11277-018-5930-z

    Article  Google Scholar 

  4. Skuratovskii, R. V., & Osadchyy, V. (2021). Analysis of the MFC singuliarities of speech signals using big data methods. In Intelligent computing (pp. 987–1009). Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_70

  5. Fu, H., & Lyu, Y. (2022). Facial recognition interaction in a university setting: impression, reaction, and decision-making. International Conference on Information. https://doi.org/10.1007/978-3-030-96957-8_29

    Article  Google Scholar 

  6. Lee, J., Jatowt, A., & Kim, K. S. (2021). Discovering underlying sensations of human emotions based on social media. Journal of the Association for Information Science and Technology, 72(4), 417–432. https://doi.org/10.1002/asi.24414

    Article  Google Scholar 

  7. Tan, Z., Dai, N., Su, Y., Zhang, R., Li, Y., Wu, D., & Li, S. (2021). Human–machine interaction in intelligent and connected vehicles: a review of status quo, issues and opportunities. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3127217

    Article  Google Scholar 

  8. Sahni, S. P., & Langan, L. (2021). Psychological approaches to detection of deceit. In Criminal psychology and the criminal justice system in India and beyond (pp. 173–184). Springer, Singapore. https://doi.org/10.1007/978-981-16-4570-9_11

  9. Quasim, M. T., Alkhammash, E. H., Khan, M. A., & Hadjouni, M. (2021). Emotion-based music recommendation and classification using machine learning with IoT Framework. Soft Computing, 25(18), 12249–12260. https://doi.org/10.1007/s00500-021-05898-9

    Article  Google Scholar 

  10. Nayak, S., Nagesh, B., Routray, A., & Sarma, M. (2021). A human–computer interaction framework for emotion recognition through time-series thermal video sequences. Computers & Electrical Engineering, 93, 107280. https://doi.org/10.1016/j.compeleceng.2021.107280

    Article  Google Scholar 

  11. Veeranki, Y. R., Kumar, H., Ganapathy, N., Natarajan, B., & Swaminathan, R. (2021). A systematic review of sensing and differentiating dichotomous emotional states using audio-visual stimuli. IEEE Access, 9, 124434–124451. https://doi.org/10.1109/ACCESS.2021.3110773

    Article  Google Scholar 

  12. Saleem, S., Amin, J., Sharif, M., Anjum, M. A., Iqbal, M., & Wang, S. H. (2021). A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-021-00473-z

    Article  Google Scholar 

  13. Yang, K., Wang, C., Gu, Y., Sarsenbayeva, Z., Tag, B., Dingler, T., Wadley, G., & Goncalves, J. (2021). Behavioral and physiological signals-based deep multimodal approach for mobile emotion recognition. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2021.3100868

    Article  Google Scholar 

  14. Aslam, A. R., & Altaf, M. A. B. (2021). A 10.13 µJ/Classification 2-channel deep neural network based SoC for negative emotion outburst detection of autistic children. IEEE Transactions on Biomedical Circuits and Systems, 15(5), 1039–1052. https://doi.org/10.1109/TBCAS.2021.3113613

    Article  Google Scholar 

  15. Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2021). A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science. https://doi.org/10.1007/s40745-021-00344-x

    Article  Google Scholar 

  16. Krishnan, P. T., Joseph Raj, A. N., & Rajangam, V. (2021). Emotion classification from speech signal based on empirical mode decomposition and non-linear features. Complex & Intelligent Systems, 7(4), 1919–1934. https://doi.org/10.1007/s40747-021-00295-z

    Article  Google Scholar 

  17. Yuvaraj, N., Chang, V., Gobinathan, B., Pinagapani, A., Kannan, S., Dhiman, G., & Rajan, A. R. (2021). Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification. Computers & Electrical Engineering, 92, 107186. https://doi.org/10.1016/j.compeleceng.2021.107186

    Article  Google Scholar 

  18. Yolcu, O. C., Temel, F. A., & Kuleyin, A. (2021). New hybrid predictive modeling principles for ammonium adsorption: The combination of Response Surface Methodology with feed-forward and Elman-Recurrent Neural Networks. Journal of Cleaner Production, 311, 127688. https://doi.org/10.1016/j.jclepro.2021.127688

    Article  Google Scholar 

  19. Dhavakumar, P., & Gopalan, N. P. (2021). An efficient parameter optimization of software reliability growth model by using chaotic grey wolf optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 12(2), 3177–3188. https://doi.org/10.1007/s12652-020-02476-z

    Article  Google Scholar 

  20. Alamiedy, T. A., Anbar, M., Alqattan, Z. N. M., & Alzubi, Q. M. (2020). Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. Journal of Ambient Intelligence and Humanized Computing, 11(9), 3735–3756. https://doi.org/10.1007/s12652-019-01569-8

    Article  Google Scholar 

  21. Srivastava, A. K., Kumar, S., & Zareapoor, M. (2018). Self-organized design of virtual reality simulator for identification and optimization of healthcare software components. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-1100-0

    Article  Google Scholar 

  22. Abualigah, L., Diabat, A., & Elaziz, M. A. (2021). Improved slime mould algorithm by opposition-based learning and Levy flight distribution for global optimization and advances in real-world engineering problems. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03372-w

    Article  Google Scholar 

  23. Shirmarz, A., & Ghaffari, A. (2021). Taxonomy of controller placement problem (CPP) optimization in Software Defined Network (SDN): A survey. Journal of Ambient Intelligence and Humanized Computing, 12(12), 10473–10498. https://doi.org/10.1007/s12652-020-02754-w

    Article  Google Scholar 

  24. Karpagam, M., Geetha, K., & Rajan, C. (2021). A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques. Journal of Ambient Intelligence and Humanized Computing, 12(2), 3199–3207. https://doi.org/10.1007/s12652-020-02480-3

    Article  Google Scholar 

  25. Kumar, P., Shilpi, S., Kanungo, A., Gupta, V., & Gupta, N. K. (2022). A novel ultra wideband antenna design and parameter tuning using hybrid optimization strategy. Wireless Personal Communications, 122(2), 1129–1152. https://doi.org/10.1007/s11277-021-08942-x

    Article  Google Scholar 

  26. Wei, L., Changwu, X., Yue, H., Liguo, C., Lining, S., & Guoqiang, F. (2019). Actual deviation correction based on weight improvement for 10-unit Dolph–Chebyshev array antennas. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1713–1726. https://doi.org/10.1007/s12652-017-0589-y

    Article  Google Scholar 

  27. Kargar-Barzi, A., & Mahani, A. (2020). H-V scan and diagonal trajectory: Accurate and low power localization algorithms in WSNs. Journal of Ambient Intelligence and Humanized Computing, 11(7), 2871–2882. https://doi.org/10.1007/s12652-019-01406-y

    Article  Google Scholar 

  28. Di Fazio, A. R., Erseghe, T., Ghiani, E., Murroni, M., Siano, P., & Silvestro, F. (2013). Integration of renewable energy sources, energy storage systems, and electrical vehicles with smart power distribution networks. Journal of Ambient Intelligence and Humanized Computing, 4(6), 663–671. https://doi.org/10.1007/s12652-013-0182-y

    Article  Google Scholar 

  29. Malhat, H. A., Zainud-Deen, A. S., Rihan, M., & Badway, M. M. (2022). Elements failure detection and radiation pattern correction for time-modulated linear antenna arrays using particle swarm optimization. Wireless Personal Communications. https://doi.org/10.1007/s11277-022-09645-7

    Article  Google Scholar 

  30. Grewal, N. S., Rattan, M., & Patterh, M. S. (2017). A non-uniform circular antenna array failure correction using firefly algorithm. Wireless Personal Communications, 97(1), 845–858. https://doi.org/10.1007/s11277-017-4540-5

    Article  Google Scholar 

  31. Talaat, F. M., & Gamel, S. A. (2022). RL based hyper-parameters optimization algorithm (ROA) for convolutional neural network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03788-y

    Article  Google Scholar 

  32. Rafiq, M. S., Jianshe, X., Arif, M., & Barra, P. (2021). Intelligent query optimization and course recommendation during online lectures in E-learning system. Journal of Ambient Intelligence and Humanized Computing, 12(11), 10375–10394. https://doi.org/10.1007/s12652-020-02834-x

    Article  Google Scholar 

  33. Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A., & Ali, S. H. (2020). A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4951–4966. https://doi.org/10.1007/s12652-020-01768-8

    Article  Google Scholar 

  34. Prabhakar, T. S., & Veena, M. N. (2022). Efficient anomaly detection using deer hunting optimization algorithm via adaptive deep belief neural network in mobile network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03861-6

    Article  Google Scholar 

  35. Gupta, V., Mittal, M., Mittal, V., & Gupta, A. (2021). ECG signal analysis using CWT, spectrogram and autoregressive technique. Iran Journal of Computer Science, 4(4), 265–280. https://doi.org/10.1007/s42044-021-00080-8

    Article  Google Scholar 

  36. Jazayeri, F., Shahidinejad, A., & Ghobaei-Arani, M. (2021). Autonomous computation offloading and auto-scaling the in the mobile fog computing: A deep reinforcement learning-based approach. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8265–8284. https://doi.org/10.1007/s12652-020-02561-3

    Article  Google Scholar 

  37. Usharani, R., & Shanthini, A. (2021). Neuropathic complications: Type II diabetes mellitus and other risky parameters using machine learning algorithms. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-02972-w

    Article  Google Scholar 

  38. Jayavadivel, R., & Prabaharan, P. (2021). Investigation on automated surveillance monitoring for human identification and recognition using face and iris biometric. Journal of Ambient Intelligence and Humanized Computing, 12(11), 10197–10208. https://doi.org/10.1007/s12652-020-02787-1

    Article  Google Scholar 

  39. Elavarasan, D., & Vincent, P. M. (2021). A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. Journal of Ambient Intelligence and Humanized Computing, 12(11), 10009–10022. https://doi.org/10.1007/s12652-020-02752-y

    Article  Google Scholar 

  40. Parameswari, C., & Siva Ranjani, S. (2021). Prediction of atherosclerosis pathology in retinal fundal images with machine learning approaches. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6701–6711. https://doi.org/10.1007/s12652-020-02294-3

    Article  Google Scholar 

  41. Kumar, K. H., & Srinivas, K. (2021). Preliminary performance study of a brief review on machine learning techniques for analogy based software effort estimation. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03427-y

    Article  Google Scholar 

  42. Benamrane, A., Benelallam, I., & Bouyakhf, E. H. (2020). Constraint programming based techniques for medical resources optimization: Medical internships planning. Journal of Ambient Intelligence and Humanized Computing, 11(9), 3801–3810. https://doi.org/10.1007/s12652-019-01587-6

    Article  Google Scholar 

  43. Gupta, V., Mittal, M., Mittal, V., & Chaturvedi, Y. (2022). Detection of R-peaks using fractional Fourier transform and principal component analysis. Journal of Ambient Intelligence and Humanized Computing, 13(2), 961–972. https://doi.org/10.1007/s12652-021-03484-3

    Article  Google Scholar 

  44. Wang, J., Wang, M., Liu, Q., Yin, G., & Zhang, Y. (2020). Deep anomaly detection in expressway based on edge computing and deep learning. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02574-y

    Article  Google Scholar 

  45. Lin, D., Li, Y., Xie, S., New, T. L., & Dong, S. (2021). Ddr-id: Dual deep reconstruction networks based image decomposition for anomaly detection. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03425-0

    Article  Google Scholar 

  46. Singh, L., & Alam, A. (2022). An efficient hybrid methodology for an early detection of breast cancer in digital mammograms. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03895-w

    Article  Google Scholar 

  47. Hafeez, U., Umer, M., Hameed, A., Mustafa, H., Sohaib, A., Nappi, M., & Madni, H. A. (2022). A CNN based coronavirus disease prediction system for chest X-rays. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03775-3

    Article  Google Scholar 

  48. Nimmy, K., Dilraj, M., Sankaran, S., & Achuthan, K. (2022). Leveraging power consumption for anomaly detection on IoT devices in smart homes. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-04110-6

    Article  Google Scholar 

  49. Song, X., Cong, Y., Song, Y., Chen, Y., & Liang, P. (2021). A bearing fault diagnosis model based on CNN with wide convolution kernels. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03177-x

    Article  Google Scholar 

  50. Hassan, T., Akçay, S., Bennamoun, M., Khan, S., & Werghi, N. (2021). Unsupervised anomaly instance segmentation for baggage threat recognition. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03383-7

    Article  Google Scholar 

  51. Deepak, S., & Ameer, P. M. (2021). Automated categorization of brain tumor from mri using cnn features and svm. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8357–8369. https://doi.org/10.1007/s12652-020-02568-w

    Article  Google Scholar 

  52. Karthik, K., & Sowmya Kamath, S. (2022). MSDNet: A deep neural ensemble model for abnormality detection and classification of plain radiographs. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03835-8

    Article  Google Scholar 

  53. Jan, A., & Khan, G. M. (2022). Real-world malicious event recognition in CCTV recording using Quasi-3D network. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03702-6

    Article  Google Scholar 

  54. Sreeja, M. U., & Kovoor, B. C. (2022). A multi-stage deep adversarial network for video summarization with knowledge distillation. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03641-8

    Article  Google Scholar 

  55. Bhangale, K. B., & Kothandaraman, M. (2022). Survey of deep learning paradigms for speech processing. Wireless Personal Communications. https://doi.org/10.1007/s11277-022-09640-y

    Article  Google Scholar 

  56. Wang, C., Jin, Y., Chen, X., & Liu, Z. (2020). Automatic classification of volumetric optical coherence tomography images via recurrent neural network. Sensing and Imaging, 21(1), 1–15. https://doi.org/10.1007/s11220-020-00299-y

    Article  Google Scholar 

  57. Shirgan, S. S., & Bombale, U. L. (2020). Hybrid neural network based wideband spectrum behavior sensing predictor for cognitive radio application. Sensing and Imaging, 21(1), 1–21. https://doi.org/10.1007/s11220-020-00293-4

    Article  Google Scholar 

  58. Salem, A., Ushijima, K., Gamey, T. J., & Ravat, D. (2001). Automatic detection of UXO from airborne magnetic data using a neural network. Subsurface Sensing Technologies and Applications, 2(3), 191–213. https://doi.org/10.1023/A:1011918119491

    Article  Google Scholar 

  59. Shao, X., Li, H., Lin, H., Kang, X., & Lu, T. (2017). Ship detection in optical satellite image based on RX method and PCAnet. Sensing and Imaging, 18(1), 1–18. https://doi.org/10.1007/s11220-017-0167-6

    Article  Google Scholar 

  60. Plaza-del-Arco, F. M., Molina-González, M. D., Ureña-López, L. A., & Martín-Valdivia, M. T. (2021). Comparing pre-trained language models for Spanish hate speech detection. Expert Systems with Applications, 166, 114120. https://doi.org/10.1016/j.eswa.2020.114120

    Article  Google Scholar 

  61. Banerjee, D., Dutta, S., & Ghosal, A. (2021). Automatic gender identification through speech. In Emerging technologies in data mining and information security (pp. 375–383). Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_36

  62. Revathi, A., Sasikaladevi, N., & Geetha, K. (2021). Forensic investigation for twin identification from speech: Perceptual and gamma-tone features and models. Multimedia Tools and Applications, 80(12), 18301–18315. https://doi.org/10.1007/s11042-021-10639-z

    Article  Google Scholar 

  63. Loizou, C. P. (2021). An automated integrated speech and face image analysis system for the identification of human emotions. Speech Communication, 130, 15–26. https://doi.org/10.1016/j.specom.2021.04.001

    Article  Google Scholar 

  64. Fasounaki, M., Yüce, E. B., Öncül, S., & İnce, G. (2021). CNN-based Text-independent automatic speaker identification using short utterances. In 2021 6th international conference on computer science and engineering (UBMK). IEEE. https://doi.org/10.1109/UBMK52708.2021.9559031

  65. Lin, J. C. W., Shao, Y., Djenouri, Y., & Yun, U. (2021). ASRNN: A recurrent neural network with an attention model for sequence labeling. Knowledge-Based Systems, 212, 106548. https://doi.org/10.1016/j.knosys.2020.106548

    Article  Google Scholar 

  66. Wankhade, S. B., & Doye, D. D. (2019). IKKN predictor: An EEG signal based emotion recognition for HCI. Wireless Personal Communications, 107(2), 1135–1153. https://doi.org/10.1007/s11277-019-06328-8

    Article  Google Scholar 

  67. Agarwal, G., & Om, H. (2021). Performance of deer hunting optimization based deep learning algorithm for speech emotion recognition. Multimedia Tools and Applications, 80(7), 9961–9992. https://doi.org/10.1007/s11042-020-10118-x

    Article  Google Scholar 

  68. Fang, Q., Nguyen, H., Bui, X. N., Nguyen-Thoi, T., & Zhou, J. (2021). Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model. Neural Computing and Applications, 33(8), 3503–3519. https://doi.org/10.1007/s00521-020-05197-8

    Article  Google Scholar 

  69. Rajak, R., & Mall, R. (2019). Emotion recognition from audio, dimensional and discrete categorization using CNNs. In TENCON 2019–2019 IEEE Region 10 conference (TENCON). IEEE. https://doi.org/10.1109/TENCON.2019.8929459

  70. Hizlisoy, S., Yildirim, S., & Tufekci, Z. (2021). Music emotion recognition using convolutional long short term memory deep neural networks. Engineering Science and Technology, an International Journal, 24(3), 760–767. https://doi.org/10.1016/j.jestch.2020.10.009

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Geetanjali College of Engineering and Technology, Hyderabad, Telangana, 501301, India

    Neeraja Koppula

  2. Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, 500043, India

    Koppula Srinivas Rao

  3. Department of Computer Science and Engineering, Sreyas Institute of Engineering and Technology, Hyderabad, Telangana, 500068, India

    Shaik Abdul Nabi

  4. Department of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, 500043, India

    Allam Balaram

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Koppula, N., Rao, K.S., Nabi, S.A. et al. A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification. Wireless Pers Commun 128, 2217–2243 (2023). https://doi.org/10.1007/s11277-022-10040-5

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