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| Usefullness of Speech Coding in Voice Banking
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Full
text: |
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Source |
Signal Processing: An International Journal (SPIJ) |
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Table of Contents |
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Complete Issue PDF(1.45MB) |
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Volume: 3 Issue: 4 |
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Pages: 34-82 |
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Publication
Date: August 2009 |
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ISSN
(Online): 1985-2339 |
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Pages |
42 - 54 |
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Author(s) |
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Published
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21-10-2009 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Voice banking, Product Code Vector Quantizers, Linear Predictive Coefficients, Line Spectral Frequencies |
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| Voice banking is an excellent telephone banking service by which a user can access his account for any service at any time of a day, in a year. The speech techniques involved in voice banking are speech coding and speech recognition. This paper investigates the performance of a speech recognizer for a coded output at 20 bits/frame obtained by using various vector quantization techniques namely Split Vector Quantization, Multi Stage Vector Quantization, Split-Multi Stage Vector Quantization, Switched Split Vector Quantization using Hard decision scheme, Switched Multi Stage Vector Quantization using Soft decision scheme and Multi Switched Split Vector Quantization using Hard decision scheme techniques. The speech recognition technique used for recognition of the coded speech signal is the Hidden Markov Model technique and the speech enhancement technique used for enhancing the coded speech signal is the Spectral Subtraction technique. The performance of vector quantization is measured in terms of spectral distortion in decibels, computational complexity in Kflops/frame, and memory requirements in floats. The performance of the speech recognizer for coded outputs at 20 bits/frame has been examined and it is found that the speech recognizer has better percentage probability of recognition for the coded output obtained using Multi Switched Split Vector Quantization using Hard decision scheme. It is also found that the probability of recognition for various coding techniques has been varied from 80% to 100%. |
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Paliwal K.K, Atal, B.S. “Efficient vector quantization of LPC Parameters at 24 bits/frame”. in: Proc.IEEE Int. Conf. Acoust, Speech, Signal Processing, (1): 661-664,1991.
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| 2 |
Paliwal. K.K, Atal, B.S. “Efficient vector quantization of LPC Parameters at 24 bits/frame”. IEEE Trans. Speech Audio Process, 1(1): 3-14, 1993.
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| 3 |
Paliwal K.K, Atal, B.S. “Efficient vector quantization of LPC Parameters at 24 bits/frame”. in: Proc.IEEE Int. Conf. Acoust, Speech, Signal Processing, (1): 661-664,1991.
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| 4 |
Paliwal. K.K, Atal, B.S. “Efficient vector quantization of LPC Parameters at 24 bits/frame”. IEEE Trans. Speech Audio Process, 1(1): 3-14, 1993.
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| 5 |
3. Stephen, So., Paliwal, K. K. “Efficient product code vector quantization using switched split vector quantizer”. Digital Signal Processing journal, Elsevier, 17(1): 138-171,2007.
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| 6 |
4. Woo-Jin Han, Eun-Kyoung Kim, Yung-Hwan Oh. “Multi codebook Split Vector Quantization of LSF parameters”. IEEE Trans. Signal Processing, 9: 418-421, 2002.
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| 7 |
5. K.Srinivas, M.Madhavi Latha, P.Siddaiah, M.satya Sai Ram.(July 2008a). Switched Multi stage vector quantization using soft decision scheme. Proceedings of the 2008 International conference on Image processing, computer vision and pattern recognition (IPCV 2008), WORLD COMP2008, 2: 587-591, 2008.
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6. M.Satya Sai Ram, P.Siddaiah, M.MadhaviLatha. “Multi Switched Split Vector Quantization of Narrow Band Speech Signals”, Proceedings World Academy of Science, Engineering and Technology, WASET, 27: 236-239,2008.
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| 9 |
7. M.Satya Sai Ram, P.Siddaiah, M.MadhaviLatha. “Multi Switched Split Vector Quantizer”, International Journal of Computer, Information, and Systems science, and Engineering, IJCISSE, WASET, 2(1): 1- 6, 2008.
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| 10 |
8. Kleijn, W.B. Tom Backstrom, Paavo Alku.”On Line Spectral Frequencies”. IEEE Signal Processing Letters, 10(3): 75-77, 2003.
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| 11 |
9. Soong. F, Juang. B. “Line spectrum pair (LSP) and speech data compression”, IEEE Conference. On Acoustics, Speech Signal Processing, 9(1): 37-40, 1984.
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| 12 |
10. Rabiner Lawrence, Juang Bing-Hwang. “Fundamentals of speech Recognition”. Prentice Hall, New Jersey, ISBN 0-13-015157-2, 1993.
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| 13 |
9. Soong. F, Juang. B. “Line spectrum pair (LSP) and speech data compression”, IEEE Conference. On Acoustics, Speech Signal Processing, 9(1): 37-40, 1984.
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| 14 |
10. Rabiner Lawrence, Juang Bing-Hwang. “Fundamentals of speech Recognition”. Prentice Hall, New Jersey, ISBN 0-13-015157-2, 1993.
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| 15 |
11. Rabiner L.R. “A tutorial on Hidden Markov Models and selected applications in speech recognition”. Poceedings of the IEEE, 77(2): 257-286, 1989.
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| 16 |
12. Robert Dale. “Dialog Design Specification: A Voice Banking Application,” Macquarie University - Sydney, 0.1: 1-22, 2002.
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| M Satya Sai Ram : Colleagues
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| P. Siddaiah : Colleagues
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| M. Madhavi Latha : Colleagues
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