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Spotting the Aberration Spot in a Speech with the Aid of Fuzzy Inference System


Affiliations
1 Research Scholar, Sathyabama University, Assistant Professor, Department of ECE,, Vel Tech University, Avadi, Chennai, India
2 Professor, Department of MCA, St. Joseph’s College of Engineering, Chennai, India
 

A wide range of researches are carried out in this field for denoising, enhancement and more. Besides the other, stress management is important to identify the spot in which the stress has to be made in speech. In this paper, in order to provide proper speech practice for the abnormal child (mentally retarded (MR) child), their speech is analyzed. Initially, the normal and abnormal children speech is obtained with the same set of words. As an initial process, the Mel Frequency Cepstrum Coefficients (MFCC) is extracted from both words and the Principal Component Analysis (PCA) is applied to reduce the dimensionality of the words. From the dimensionality reduced words, the parameters are obtained and then these parameters are utilized to train using Support Vector Machines (SVM) for classification. After identifying the acute word (abnormal word), through the thresholding operation and then FFT is computed for the acute word and these parameters made use of the Fuzzy Inference system (FIS) for blemishing the acute spot in which the aberration is occurred in the world where the speech practice is required for the abnormal child which helps speech pathologist.

Keywords

Speech Signal, Stress, Mel Frequency Cepstrum Coefficients (MFCC), Principal Component Analysis (PCA), Support Vector Machines (SVM), Fuzzy Inference System (FIS).
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  • Bharathi CR., Shanthi V, (2011). Classification of speech for Clinical Data using Artificial Neural Network. IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 1, November 2011 ISSN (Online): 1694-0814

  • Bharathi CR., Shanthi V, (2012). Disorder Speech Clustering For Clinical Data Using Fuzzy C-Means Clustering and Comparison With SVM classification. Indian Journal of Computer Science and Engineering (IJCSE), Vol. 3, No.5

  • Bharathi, CR., Shanthi V, (2012). Discriminant Analysis of Disorder Speech For Clinical Data. European Journal of Scientific Research, Volume 90.

  • Bharathi CR., Shanthi V, (2012). Disorder Speech Classification for Clinical Data using SVM”, National Conference, IETE.

  • Bharathi CR., Shanthi V, (2011). Finding acute Peaks and Amplitudes of Speech for Clinical Data using FFT. ICACM, International conference,

  • Elseiver – 2011

  • Bharathi CR., Shanthi, V, (2011). MFCC Feature Extraction Algorithm for Clinical Data. NCCCES’11, pp. 103-106.

  • Bharathi CR., Shanthi V, (2011). Feature Extraction using MFCC and Survey on Classification Algorithms for Clinical Data. International Conference on Computer Science Engineering CSE – 2011

  • Bharathi CR., Shanthi V, (2012). Survey on Objective Assessment of Stuttered Speech Signal for Disabled Children. International Conference on Cloud Computing and eGovernance.

  • Bharathi CR., Shanthi V, (2012). An Effective System for Acute Spotting Aberration in the Speech of Abnormal Children Via Artificial Neural Network and Genetic Algorithm. American Journal of Applied Sciences 9 (10): 1561-1570.

  • Sven Nordholm, Thushara Abhayapala, Simon Doclo, Sharon Gannot, Patrick Naylor and Ivan Tashev, (2010). Microphone Array Speech Processing. EURASIP Journal on Advances in Signal Processing. pp. 1-3, 2010

  • Marius Crisan, (2007). Chaos and Natural Language Processing. Acta Polytechnica Hungarica, Vol. 4, No. 3, pp. 61-74.

  • Rashad, Hazem M. El-Bakry and Islam R. Ismail (2010). Diphone Speech Synthesis System for Arabic Using MARY TTS. International journal of computer science & information Technology (IJCSIT), Vol. 2, No. 4, pp. 18-26.

  • Stelzle, Ugrinovic, Knipfer, Bocklet, Noth, Schuster, Eitner, Seiss and Nkenke, (2010). Automatic, computer-based speech assessment on edentulous patients with and without complete dentures - preliminary results. Journal of Oral Rehabilitation, Vol.37, No. 3, pp. 209-216.


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  • Spotting the Aberration Spot in a Speech with the Aid of Fuzzy Inference System

Abstract Views: 431  |  PDF Views: 65

Authors

C. R. Bharathi
Research Scholar, Sathyabama University, Assistant Professor, Department of ECE,, Vel Tech University, Avadi, Chennai, India
V. Shanthi
Professor, Department of MCA, St. Joseph’s College of Engineering, Chennai, India

Abstract


A wide range of researches are carried out in this field for denoising, enhancement and more. Besides the other, stress management is important to identify the spot in which the stress has to be made in speech. In this paper, in order to provide proper speech practice for the abnormal child (mentally retarded (MR) child), their speech is analyzed. Initially, the normal and abnormal children speech is obtained with the same set of words. As an initial process, the Mel Frequency Cepstrum Coefficients (MFCC) is extracted from both words and the Principal Component Analysis (PCA) is applied to reduce the dimensionality of the words. From the dimensionality reduced words, the parameters are obtained and then these parameters are utilized to train using Support Vector Machines (SVM) for classification. After identifying the acute word (abnormal word), through the thresholding operation and then FFT is computed for the acute word and these parameters made use of the Fuzzy Inference system (FIS) for blemishing the acute spot in which the aberration is occurred in the world where the speech practice is required for the abnormal child which helps speech pathologist.

Keywords


Speech Signal, Stress, Mel Frequency Cepstrum Coefficients (MFCC), Principal Component Analysis (PCA), Support Vector Machines (SVM), Fuzzy Inference System (FIS).

References