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Spoken language identification based on optimised genetic algorithm–extreme learning machine approach

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Abstract

The determination and classification of a recognized spoken language based on certain contents and datasets is known as the process of language identification (LID). The common process in carrying out LID entails the mandatory processing of data which enables the extraction of the necessary features for the process. The extraction involves a mature process whereby the development of the standard LID features have been conducted much earlier by means of a mel-frequency cepstral coefficients, shifted delta cepstral, Gaussian mixture model and i-vector-based framework. Despite that, improvement or rather optimisation still needs to be done on the learning process based on the extracted features so as to obtain all the knowledge embedded within them. The classification and regression analysis can benefit tremendously from the use of the extreme learning machine (ELM) which is a particularly effective and useful learning model for training a single-hidden layer neural network. However, owing to the randomly selected weights embedded in the input’s hidden layers, the model’s learning process is rendered to be ineffective or not optimised in its entirety. In this study, the ELM is employed as the learning model for LID due to the standard feature extraction. In addition, this study proposes a new optimised genetic algorithm (OGA) with three different selection criteria (i.e., roulette wheel, K-tournament and random) to select the appropriate initial weights and biases of the input hidden layer of the ELM, thereby minimising the classification error and improving the general performance of the ELM for LID. Results show the excellent performance of the proposed OGA–ELM with three different selection criteria, namely, roulette wheel, K-tournament and random, with the highest accuracies of 99.50%, 100% and 99.38%, respectively.

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References

  • Albadr, M. A. A., et al. (2018). Spoken language identification based on the enhanced self-adjusting extreme learning machine approach. PLoS ONE, 13(4), e0194770.

    Article  Google Scholar 

  • Albadra, M. A. A., & Tiuna, S. (2017). Extreme learning machine: A review. International Journal of Applied Engineering Research, 12(14), 4610–4623.

    Google Scholar 

  • Andrushia, A. D., & Thangarajan, R. (2019). RTS-ELM: An approach for saliency-directed image segmentation with ripplet transform (pp. 1–13). Pattern Analysis and Applications.

  • Atee, H. A., et al. (2016). A novel extreme learning machine-based cryptography system. Security and Communication Networks, 9(18), 5472–5489.

    Article  Google Scholar 

  • Bi, C. (2010). Deterministic local alignment methods improved by a simple genetic algorithm. Neurocomputing, 73(13–15), 2394–2406.

    Article  Google Scholar 

  • Contreras-Bolton, C., & Parada, V. (2015). Automatic combination of operators in a genetic algorithm to solve the traveling salesman problem. PLoS ONE, 10(9), e0137724.

    Article  Google Scholar 

  • Deng, C., et al. (2015). Extreme learning machines: New trends and applications. Science China Information Sciences, 58(2), 1–16.

    Article  Google Scholar 

  • Garg, A., Gupta, V., & Jindal, M. (2014). A survey of language identification techniques and applications. Journal of Emerging Technologies in Web Intelligence, 6(4), 388–400.

    Google Scholar 

  • Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.

    Article  Google Scholar 

  • Hafen, R. P., & Henry, M. J. (2012). Speech information retrieval: A review. Multimedia Systems, 18(6), 499–518.

    Article  Google Scholar 

  • Han, K., Yu, D., & Tashev, I. (2014). Speech emotion recognition using deep neural network and extreme learning machine. In Fifteenth annual conference of the international speech communication association.

  • Holland, J. H. (1975). Adaption in natural and artificial systems. An introductory analysis with application to biology, control and artificial intelligence. Ann Arbor: University of Michigan Press.

    MATH  Google Scholar 

  • Huang, G.-B. (2014). An insight into extreme learning machines: Random neurons, random features and kernels. Cognitive Computation, 6(3), 376–390.

    Article  Google Scholar 

  • Huang, G.-B., Chen, L., & Siew, C. K. (2006a). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17(4), 879–892.

    Article  Google Scholar 

  • Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006b). Extreme learning machine: Theory and applications. Neurocomputing, 70(1), 489–501.

    Article  Google Scholar 

  • Huang, G.-B., et al. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513–529.

    Article  Google Scholar 

  • Huang, G., et al. (2014). Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 44(12), 2405–2417.

    Article  Google Scholar 

  • Iosifidis, A., Tefas, A., & Pitas, I. (2016). Graph embedded extreme learning machine. IEEE Transactions on Cybernetics, 46(1), 311–324.

    Article  Google Scholar 

  • Jiang, B., et al. (2014). Deep bottleneck features for spoken language identification. PLoS ONE, 9(7), e100795.

    Article  Google Scholar 

  • Lan, Y., et al. (2013). An extreme learning machine approach for speaker recognition. Neural Computing and Applications, 22(3–4), 417–425.

    Article  Google Scholar 

  • Lefebvre, G., & Cumin, J. (2016). Recognizing human actions based on extreme learning machines. In 11th international joint conference on computer vision, imaging and computer graphics theory and applications.

  • Li, J., et al. (2015). LSTM time and frequency recurrence for automatic speech recognition. In 2015 IEEE workshop on automatic speech recognition and understanding (ASRU). IEEE.

  • Liang, N.-Y., et al. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17(6), 1411–1423.

    Article  Google Scholar 

  • Liu, B., et al. (2016). Manifold regularized extreme learning machine. Neural Computing and Applications, 27(2), 255–269.

    Article  Google Scholar 

  • Michalewicz, Z., & Hartley, S. J. (1996). Genetic algorithms + data structures = evolution programs. Mathematical Intelligencer, 18(3), 71.

    Article  Google Scholar 

  • Mohamed, M. H. (2011). Rules extraction from constructively trained neural networks based on genetic algorithms. Neurocomputing, 74(17), 3180–3192.

    Article  Google Scholar 

  • Nayak, P., et al. (2016). Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances. Neural Computing and Applications, 27(7), 2107–2122.

    Article  Google Scholar 

  • Niu, P., et al. (2016). A kind of parameters self-adjusting extreme learning machine. Neural Processing Letters, 44(3), 813–830.

    Article  Google Scholar 

  • Padmanabhan, S. A., & Kanchikere, J. (2019). An efficient face recognition system based on hybrid optimized KELM (pp. 1–21). Multimedia Tools and Applications.

  • Pal, M., Maxwell, A. E., & Warner, T. A. (2013). Kernel-based extreme learning machine for remote-sensing image classification. Remote Sensing Letters, 4(9), 853–862.

    Article  Google Scholar 

  • Rujirakul, K., & So-In, C. (2018) Histogram equalized deep PCA with ELM classification for expressive face recognition. In 2018 international workshop on advanced image technology (IWAIT). IEEE.

  • Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. In Australasian joint conference on artificial intelligence. Berlin: Springer.

  • Wang, Y., Cao, F., & Yuan, Y. (2011). A study on effectiveness of extreme learning machine. Neurocomputing, 74(16), 2483–2490.

    Article  Google Scholar 

  • Xiang, J., et al. (2014). Using extreme learning machine for intrusion detection in a big data environment. In: Proceedings of the 2014 workshop on artificial intelligent and security workshop. ACM.

  • Xu, J., et al. (2015). Regularized minimum class variance extreme learning machine for language recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2015(1), 22.

    Article  Google Scholar 

  • Yaacob, S., Muthusamy, H., & Polat, K. (2015). Improved emotion recognition using gaussian mixture model and extreme learning machine in speech and glottal signals. Mathematical Problems in Engineering. https://doi.org/10.1155/2015/394083.

    Google Scholar 

  • Yang, Z., Zhang, T., & Zhang, D. (2016). A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training. Cognitive Neurodynamics, 10(1), 73–83.

    Article  Google Scholar 

  • Zazo, R., et al. (2016). Language identification in short utterances using long short-term memory (LSTM) recurrent neural networks. PLoS ONE, 11(1), e0146917.

    Article  Google Scholar 

Download references

Acknowledgements

This project was funded by the Universiti Kebangsaan Malaysia under Dana Impak Perdana Grant (Research Code: DIP-2016-033).

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Authors and Affiliations

  1. CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Musatafa Abbas Abbood Albadr, Sabrina Tiun & Masri Ayob

  2. Department of Communication Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor, Malaysia

    Fahad Taha AL-Dhief

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  1. Musatafa Abbas Abbood Albadr
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  2. Sabrina Tiun
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  3. Masri Ayob
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  4. Fahad Taha AL-Dhief
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Correspondence to Musatafa Abbas Abbood Albadr.

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Albadr, M.A.A., Tiun, S., Ayob, M. et al. Spoken language identification based on optimised genetic algorithm–extreme learning machine approach. Int J Speech Technol 22, 711–727 (2019). https://doi.org/10.1007/s10772-019-09621-w

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  • DOI: https://doi.org/10.1007/s10772-019-09621-w

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