Spoken Keyword Spotting System Design Using Various Wavelet Transformation Techniques with BPNN Classifier
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Abstract
Spoken Keyword spotting is a speech data mining task used to search audio signals for occurrences of a specified spoken word in the given speech file. It is essential to identify the occurrences of specified keywords expertly from hours of speech contents such as meetings, lectures, etc. In this paper, a keyword spotting system is designed using various wavelet transformation techniques and Backpropagation Neural Network (BPNN). The Back Propagation Neural Network (BPNN) is trained with two predefined spoken keywords based on known features, and finally, input speech features are compared with keyword features in the trained BPNN for spotting the occurrences of the specified keyword. The method is tested with ten speech contents from ten different speakers. Various statistical features extraction techniques with wavelet transformation are used, and performance comparison is done among these methods with Haar, Daubechies2, and Simlet 4 wavelets.
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References
Bahi, H., & Benati, N. (2009). A new keyword spotting approach. IEEE International Conference on Multimedia Computing and Systems, pp. 77-80.
Chui, C. K. (1992). An introduction to wavelets. Academic, New York.
Flandrin, P. (1993). Representation temps-fréquence. Hermes.
Fourier, J. (1822). The analytical theory of heat. (Trans: Freeman, A). Cambridge University Press, London, pp. 1878.
Heerman, P. D., & Khazenie, N. (1992). Classification of multispectral remote sensing data using a backpropagation neural network. IEEE Transactions on Geoscience and Remote Sensing, 30(1), 81-88.
Jo, Y. T., Shen, L., Lee, S. L., & Tan, H. H. (2000). A general approach for analysis and application of Discrete Multiwavelet Transforms. IEEE Transactions on Signal Processing, 48(2), 457-464.
Jothilakshmi, S. (2013). Spoken keyword detection using autoassociative neural networks. International Journal of Speech Technology, Springer, pp. 83-89.
Khan, W., & Holton, R. (2014). Word spotting in continuous speech using wavelet transform. IEEE International Conference on Electro/Information Technology, pp. 275-279.
Mallat, S. (1999). A wavelet Tour of Signal Processing. Academic Press, New York.
Qian, S. (2002). Time-frequency and wavelet transforms. Prentice Hall PTR, Upper Saddle River, NJ.
Rama Kishore, & Taranjit Kaur. (2012). Backpropagation Algorithm: An Artificial Neural Network Approach for Pattern Recognition. International Journal of Scientific & Engineering Research, 3(6), 1. ISSN 2229-5518.
Sangeetha, J., & Jothilakshmi, S. (2014). A novel spoken keyword spotting system using support vector machine. Engineering Applications of Artificial Intelligence, Springer, pp. 287-293.
Senthil Devi, K. A., & Srinivasan, B. (2016). A novel Keyword Spotting Algorithm in speech mining using wavelet. International Journal of Current Research, 8(8), pp. 36943-36946.
Senthil Devi, K. A., & Srinivasan, B. (2017). Wavelet - Neural Network Approach for keyword spotting in Speech Mining. International Journal of Trends and Technologies, 43(3), pp. 160-165.
Li, T., Ma, S., & Ogihara, M. (2005). Wavelet methods in data mining. In Data Mining and Knowledge Discovery Handbook (Chapter 27). Springer, pp. 603-626.