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| Comparison and Analysis Of LDM and LMS for an Application of a Speech
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Full
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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(1.28MB) |
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Volume: 5 Issue: 4 |
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Pages: NULL |
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Publication
Date: September / October 2011 |
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ISSN
(Online): 1985-2339 |
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Pages |
130 - 141 |
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Author(s) |
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Published
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05-10-2011 |
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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: Kalman Gain, Lms, Cross Correlation |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. Google Scholar |
| 4. Docstoc |
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| Most of the automatic speech recognition (ASR) systems are based on Guassian Mixtures model. The output of these models depends on subphone states. We often measure and transform the speech signal in another form to enhance our ability to communicate. Speech recognition is the conversion from acoustic waveform into written equivalent message information. The nature of speech recognition problem is heavily dependent upon the constraints placed on the speaker, speaking situation and message context. Various speech recognition systems are available. The system which detects the hidden conditions of speech is the best model. LMS is one of the simple algorithm used to reconstruct the speech and linear dynamic model is also used to recognize the speech in noisy atmosphere..This paper is analysis and comparison between the LDM and a simple LMS algorithm which can be used for speech recognition purpose. |
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M.S. Grewal and A.P. Andrews, Kalman Filtering Theory and Practice Using MATLAB 2nd edition, John Wiley & Sons, Canada, 2001, pp 1-12
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An Introduction to the Kalman FilterGreg Welch and Gary Bishop UNC-Chapel Hill, TR 95-041, July 24, 2006
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J. Frankel,”Linear Dynamic Models for automatic speech recognition”, Ph.D. dissertation, The center for Speech Technology Research, University of Edinburgh, UK, 2003.
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Joe Frankel and Simon King, speech Recognition using Linear Dynamic Models”, Member, IEEE Member,IEE Manuscript received September 2004. This work is supported by EPSRC grant GR/S21281/01 Joe Frankel and Simon King are both with the Center for speech Technology Research, University of Edinburgh.
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Discriminative training for large vocabulary speech recognition using minimum classification errors” by Eric McDermott member IEEE,TimonthyJ Hazen,Member IEEE, Jonathan Le Roux,Atsushi Nakamura,Member,IEEE,and Shigeru Katagiri,Fellow,IEEE IEEE transaction on Audio speech and language processing vol 15 No1 2007
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Kalman, R. E. 1960. "A New Approach to Linear Filtering and Prediction Problems," Transaction of the ASME--Journal of Basic Engineering, March 1960.
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Kalman-Filtering Speech Enhancement Method Based on a Voiced-Unvoiced Speech Model, Zenton Goh, Kah-Chye Tan, Senior Member, IEEE, and B. T. G. Tan IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 7, NO. 5, SEPTEMBER 1999
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The stability of variable step-size LMS algorithms Gelfand,S.B.; Yongbin Wei; Krogmeier, J.V.; Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN Signal Processing, IEEE Transactions on Issue Date Dec 1999.
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Application of optimal settings of the LMS adaptive filter for speech signal processing Computer Science and Information Technology (IMCSIT), Proceedings of the 2010.
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C. R. Watkins, “Practical Kalman Filtering in Signal Coding”, New Techniques in Signal Coding, ANU, Dec 1994.
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LMS Adaptive filtering for enhancing the quality of noisy speech Sambur, M. ITT Defense Communications Division, Nutley, N. J. Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '78.
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Application of the LMS adaptive filter to improve speech communication in the presence of noise Chabries, D. Christiansen, R. Brey, R. Robinette, M. Brigham Young University, Provo, UT, USA This paper appears in: Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '82.
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Speech enhancement using a Kalman-based normalized LMS algorithm Mahmoodzadeh, A. Abutalebi, H.R. Agahi, H. Electr. Eng. Dept., Yazd Univ., and Yazd This paper appears in: Telecommunications, 2008. IST 2008. International Symposium on Issue Date 27-28 Aug. 2008
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Reduction of nonstationary acoustic noise in speech using LMS adaptive noise cancelling Pulsipher, D. Boll, S. Rushforth, C.Timothy, L. Sandia Laboratories, Livermore, CA This paper appears in: Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '79.
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Adaptive noise canceling for speech signals Sambur, M. ITT Defense Communication Division, Nutley, NJ This paper appears in: Acoustics, Speech and Signal Processing, IEEE Transactions on Issue Date : Oct 1978
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Paper in IEEE explore entitled “Comparison of LDM and HMM for an Application of a Speech” by Mane, V.A., Patil, A.B., Paradeshi, K.P., Dept. of E&TC, Annasaheb Dange COE, Ashta, India in International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom), 2010 Issue Date: 16-17 Oct. 2010 On page(s): 431-436 Location: Kottayam Print ISBN: 978-1-4244-8093-7 References Cited: 13 INSPEC Accession Number: 11679354 Digital Object Identifier: 10.1109/ARTCom.2010.65 Date of Current Version: 03 December 2010
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| vikram Anant Mane : Colleagues
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| K.P. Paradeshi : Colleagues
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| S.A.Harage : Colleagues
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| M.S.Ingawale : Colleagues
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