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Comparison and Analysis Of LDM and LMS for an Application of a Speech
vikram Anant Mane, K.P. Paradeshi, S.A.Harage, M.S.Ingawale
Pages - 130 - 141     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 5   Issue - 4    |    Publication Date - September / October 2011  Table of Contents
Kalman Gain, Lms, Cross Correlation
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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16 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
Mr. vikram Anant Mane
ADCET - India
Mr. K.P. Paradeshi
ADCET - India
Mr. S.A.Harage
ADCET - India
Mr. M.S.Ingawale
ADCET - India