Abstract
This paper presents an Artificial Neural Network (ANN) based algorithm design to identify speakers of specific dialect using features obtained from various speaker dependent parameters of voiced speech. It is evident that speakers can be identified from their voiced sounds which have higher energy. Voice sounds are extracted from continuous speech signal from a set of trained male and female speakers. Here, feature vectors are generated from the speaker specific characteristics like pitch, linear prediction (LP) residual and empirical mode decomposition (EMD) residual of the speech. Using these feature vectors, three different ANN classifiers are designed using Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) to identify the speakers along with the dialect of the speaker. From the experiment, it is found that a hybrid classifier designed by combining all three classifiers correctly identifies more than 90% of the enrolled speakers.
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References
Badran, E.F.M.F., Selim, H.: Speaker Recognition Using Artificial Neural Networks Based on Vowel phonemes. In: In Proceedings of 5th International Conference on Signal Processing Proceedings (ICSP 2000), pp. 796–802 (2000)
Hossaina, M., Ahmedb, B., Asrafic, M.: A Real Time Speaker Identification using Artificial Neural Network. In: Proceedings of 10th International Conference on Computer and Information Technology (ICCIT 2007), Dhaka, pp. 1–5 (2007)
Campbell Jr., J.P.: Speaker Recognition: A Tutorial. Proceedings of the IEEEÂ 85(9) (1997)
Vyas, G., Kumari, B.: Speaker Recognition System Based On MFCC and DCT. International Journal of Engineering and Advanced Technology (IJEAT)Â 2(5) (2013)
Yegnanarayana, B., Sharat Reddy, K., Kishore, S.P.: Source and System Features for Speaker Recognition using AANN models. In: Proceedings of IEEE International Conference of Acoustics, Speech, Signal Processing, Salt Lake City, pp. 409–412 (2001)
Pati, D., Prasanna, S.R.M.: Speaker Recognition from Excitation Source Perspective. IETE Technical Review 27(2) (2010)
Sarma, M., Sarma, K.K.: Speaker Identification Model for Assamese Language using a Neural Framework. In: Proceedings of International Joint Conferrence on Neural Network (IJCNN), Dallas, Tx, United States (2013)
Sarma, M., Sarma, K.K.: Vowel Phoneme Segmentation for Speaker Identification Using an ANN-Based Framework. Journal of Intelligent Systems 22(2), 111–130 (2013)
Lajish, V.L., Kumar, R.K.S., Vivek, P.: Speaker Identification using a Nonlinear Speech Model and ANN. International Journal of Advanced Information Technology 2(5), 15–24 (2012)
Ranjan, R., Singh, S.K., Shukla, A., Tiwari, R.: Text-Dependent Multilingual Speaker Identification for Indian Languages Using Artificial Neural Network. In: Proceedings of 3rd International Conference on Emerging Trends in Engineering and Technology, pp. 632–635. ABV-IIITM, Gwalior (2010)
Bennani, Y., Gallnari, P.: Neural networks for discrimination and modelization of speakers. Speech Communication 17, 159–175 (1995)
Mak, M.W., Allen, W.G., Sexton, G.G.: Speaker identification using multilayer perceptron and radial basis function networks. Neurocomputing 6(1), 99–117 (1994)
Hu, Y.H., Hwang, J.N.: Handbook of Neural Network Signal Processing. The Electrical Engineering and Applied Signal Processing Series. CRC Press, USA (2002)
Goswami, G.C.: Structure of Assamese, 1st edn. Department of Publication, Gauhati University, Guwahati, Assam, India (1982)
Assamese, Resource Centre for Indian Language Technology Solutions, Indian Institute of Technology, Guwahati
Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Pearson Education, New Delhi (2009)
Markel, J.D.: The SIFT algorithm for fundamental frequency estimation. IEEE Transaction on Audio and Electroacoustics AU-20, 367–377 (1972)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shin, H.H., Zheng, Q.: The empirical mode decomposition method and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, Series A, 903–995 (1998)
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Dutta, M., Patgiri, C., Sarma, M., Sarma, K.K. (2015). Closed-Set Text-Independent Speaker Identification System Using Multiple ANN Classifiers. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_41
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DOI: https://doi.org/10.1007/978-3-319-11933-5_41
Publisher Name: Springer, Cham
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