vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffbd772b000000bd25060001000400 Over the previous years, a marvelous quantity of study was performed by utilizing the artificial intelligence based deep learning approaches for the speech recognition applications. The automatic speech recognition (ASR) facing the problems in as preprocessing, feature extraction and classification stages mostly, thus solving these problems is mandatory to improve the classification accuracy of speech processing. To solve these issues, an advanced speech recognition methodology has developed by utilizing the Spectral Subtraction (SS) method of denoising with the combination of Mel-frequency Cepstral coefficients (MFCCs) and linear predictive coefficients (LPCs) feature extraction of speech signals. Then back propagated artificial neural networks (BP-ANN) is utilized for classifying the speech signals for the purpose of ASR, respectively. The simulation results show that the proposed approach gives the better classification accuracy compared to the state-of-ASR approaches.
K Pavan Raju1, A Sri Krishna2, M Murali3
Centurion University of Technology and Management, India1,3, Shri Vishnu Engineering College for Women, India2
Speech Processing, Automatic Speech Recognition, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding, Artificial Neural Networks
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 10 , Issue: 4 , Pages: 2153-2159 )
Date of Publication :
July 2020
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115
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