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Speech Recognition by Improving the Performance of Algorithms used in Discrimination


Affiliations
1 ALIraqia University College of Engineering, Iraq
 

Speech recognition techniques are one of the most important modern technologies. Many different systems have been developed in terms of methods used in the extraction of features and methods of classification. Voice recognition includes two areas: speech recognition and speaker recognition, where the research is confined to the field of speech recognition.

The research presents a proposal to improve the performance of single word recognition systems by an algorithm that combines more than one of the techniques used in character extraction and modulation of the neural network to study the effects of recognition science and study the effect of noise on the proposed system.

In this research four systems of speech recognition were studied, the first system adopted the MFCC algorithm to extract the features. The second system adopted the PLP algorithm, while the third system was based on combining the two previous algorithms in addition to the zero-passing rate. In the fourth system, the neural network used in the differentiation process was modified and the error ratio was determined. The impact of noise on these previous systems.

The outcomes were looked at regarding the rate of recognizable proof and the season of preparing the neural network for every system independently, to get a rate of distinguishing proof and quiet up to 98% utilizing the proposed framework.


Keywords

Speech Recognition, PLP, MFCC, Artificial Neural Networks (ANN).
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  • Rama Ghassan Hassan,( 2015)“Improving the results of voice recognition based on the results of the integration of different systems”, Tishreen University, Vol. 85 No85.

  • INGE GAVAT, DIANA MILITARU,( 2015 )“New trends in machine learning in speech recognition” , SISOM Bucharest, pp 276.

  • POONAM SHARMA, ANGALI GARG,( 2016)“Feature Extraction and Recognition of Hindi Spoken Words using Neural Networks”, International Journal of Computer Applications (0975 – 8887) Volume 142 – No.7, pp 17.

  • VETON Z. KËPUSKA, HUSSIEN A. ELHARATI,( 2015)“Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC,LPCC, PLP, RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions”, Journal of Computer and Communications, , pp 1-9,

  • YUSRA FAISAL AL-IRAHYIM, LUJAIN YOUNIS ABDULKADER,( 2017)“Speaker Dependent Speech Recognition in Computer Game Control”, International Journal of Computer Applications (0975–8887)Volume 158–No 4 , , pp 37.

  • DIAMANTARAS K. AND KUNG S,( 2006)“Principle Component Neural Networks Theory and Applications”, New York, John Wiley & Sons Inc, , , pp255.

  • LAVNEET SINGH, GIRIJA CHETTY,( 2012)“A Comparative Study of Recognition of Speech Using Improved MFCC Algorithms and Rasta Filters”, Information Systems, Technology and Management Communications in Computer and Information Science Volume 285, , pp 304-.413

  • BHAVNA SHARMA, K. VENUGOPALAN,( 2014)“Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images”. IOSR Journal of Computer Engineering, Volume 16, Issue 1, pp 35.

  • PITZ, M, SCHLUTER R, NEY H, MOLAU S,( 2001 )“Computing Mel-frequency cepstral coefficients on the power spectrum”, Print ISBN: 0-7803-7041-4 INSPEC Accession Number: 7120280 Acoustics, Speech, and Signal Processing, 2001, Proceedings. (ICASSP '01). IEEE International Conference on (Volume: 1) Page 73 - 76 vol.1, pp12.

  • H. HERMANSKY,( 1989)“Perceptual linear predictive (PLP) analysis of speech”, Speech Technology Laboratory, Division of Panasonic Technologies, Inc. 3888 State Street, Santa Barbara, California 93105., pp 1752.

  • H. DEMUTH, M. BEALE,(2002)“Neural Networks Toolbox User’s Guide”. The MathWorks, Inc. pp 826.

  • P. RANI, S. KAKKAR, S. RANI,( 2015)“Speech recognition using neural networks”. International conference on advancement in engineering and technology., pp 14.

  • N. DAVE,( 2013)“Features Extraction Methods LPC, PLP and MFCC in Speech Recognition”, International Journal for Advanced Research in Engineering and Technology. Vol.1, Issue VI, July, pp5.

  • BHUSHAN C. KAMLE,( 2016)“Speech recognition using artificial neural networks”, Int’l journal of Computing, Communication & Instrumentation Engg, (IJCCIE), Vol 3, Issue 1, pp 4.

  • A. MANSOUR, G. SALH, H. Z. ALABDEN,( 2015)“Speech recognition using back propagation algorithm in neural network”, International Journal of Computer Trends and Technology(IJCTT), Vol 23,Number 3, pp21.


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  • Speech Recognition by Improving the Performance of Algorithms used in Discrimination

Abstract Views: 314  |  PDF Views: 134

Authors

Alahmar -Haeder Talib Mahde
ALIraqia University College of Engineering, Iraq

Abstract


Speech recognition techniques are one of the most important modern technologies. Many different systems have been developed in terms of methods used in the extraction of features and methods of classification. Voice recognition includes two areas: speech recognition and speaker recognition, where the research is confined to the field of speech recognition.

The research presents a proposal to improve the performance of single word recognition systems by an algorithm that combines more than one of the techniques used in character extraction and modulation of the neural network to study the effects of recognition science and study the effect of noise on the proposed system.

In this research four systems of speech recognition were studied, the first system adopted the MFCC algorithm to extract the features. The second system adopted the PLP algorithm, while the third system was based on combining the two previous algorithms in addition to the zero-passing rate. In the fourth system, the neural network used in the differentiation process was modified and the error ratio was determined. The impact of noise on these previous systems.

The outcomes were looked at regarding the rate of recognizable proof and the season of preparing the neural network for every system independently, to get a rate of distinguishing proof and quiet up to 98% utilizing the proposed framework.


Keywords


Speech Recognition, PLP, MFCC, Artificial Neural Networks (ANN).

References