ICTACT Journal on Soft Computing

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Automatic Speech Recognition System using MFCC-based LPC approach with Back Propagated Artificial Neural Networks


Affiliations
1 Department of Computer Science Engineering, Centurion University of Technology and Management, India
2 Department of Information Technology, Shri Vishnu Engineering College for Women, India
3 Department of Electronics and Communication Engineering, Centurion University of Technology and Management, India
     

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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..

Keywords

Speech Processing, Automatic Speech Recognition, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding, Artificial Neural Networks.
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  • Automatic Speech Recognition System using MFCC-based LPC approach with Back Propagated Artificial Neural Networks

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Authors

K. Pavan Raju
Department of Computer Science Engineering, Centurion University of Technology and Management, India
A. Sri Krishna
Department of Information Technology, Shri Vishnu Engineering College for Women, India
M. Murali
Department of Electronics and Communication Engineering, Centurion University of Technology and Management, India

Abstract


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..

Keywords


Speech Processing, Automatic Speech Recognition, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding, Artificial Neural Networks.

References