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Bharathi, C. R.
- Spotting the Aberration Spot in a Speech with the Aid of Fuzzy Inference System
Abstract Views :402 |
PDF Views:65
Authors
C. R. Bharathi
1,
V. Shanthi
2
Affiliations
1 Research Scholar, Sathyabama University, Assistant Professor, Department of ECE,, Vel Tech University, Avadi, Chennai, IN
2 Professor, Department of MCA, St. Joseph’s College of Engineering, Chennai, IN
1 Research Scholar, Sathyabama University, Assistant Professor, Department of ECE,, Vel Tech University, Avadi, Chennai, IN
2 Professor, Department of MCA, St. Joseph’s College of Engineering, Chennai, IN
Source
Indian Journal of Innovations and Developments, Vol 1, No 12 (2012), Pagination: 795-802Abstract
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).Full Text
References
- 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.
- Disorder Speech Classification for Clinical Data Using SVM
Abstract Views :159 |
PDF Views:3
Authors
C. R. Bharathi
1,
V. Shanthi
2
Affiliations
1 Sathyabama University, Chennai, IN
2 St. Josephs' College of Engineering, IN
1 Sathyabama University, Chennai, IN
2 St. Josephs' College of Engineering, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 6 (2012), Pagination: 318-321Abstract
In this work, mild level of mental retardation (MR) children speech samples were taken for consideration. In the existing system, there are many effective treatments for the problem of stammering. Most of these involve making changes in the manner of speaking. They are conducted by speech and language pathologists by giving fluency in speech practice in general. The proposed work is, the acute spot must be identified for affording speech training to the speech disordered children. In this paper, still classification of speech is found. Initially Feature Extraction is implemented using Mel Frequency Cepstrum Coefficients (MFCC) for both words of normal and pathological subjects' speech. Dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA). Finally the features are trained using Top of Form Support Vector Machines (SVM) for classification.Keywords
Speech Signal, Stammering, Mel Frequency Cepstrum Coefficients (MFCC), Principal Component Analysis (PCA), Trained.
- Risks Associated to Size Estimation of E-Commerce System using Function Point based Estimation Techniq
Abstract Views :144 |
PDF Views:0
Authors
Affiliations
1 Department of CSE, RVS Padhmavathy College of Engineering and Technology, Chedilpakkam - 601202, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Vel Tech University, Chennai - 600062, Tamil Nadu
1 Department of CSE, RVS Padhmavathy College of Engineering and Technology, Chedilpakkam - 601202, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Vel Tech University, Chennai - 600062, Tamil Nadu
Source
Indian Journal of Science and Technology, Vol 9, No 7 (2016), Pagination:Abstract
Background/Objectives: To identify the risks associated to the size estimation of E-commerce systems with the help of existing and new metrics and values using function point based estimation techniques. Methods/Statistical Analysis: An innovative combined approach is required for finding the risks associated to the size estimation of E-Commerce system. Here we are using Function points, Risk analysis techniques and the basic size estimation techniques like Line of Codes, DELPHI etc. Findings: E-Commerce system is a versatile software having function points with variable behavior. Ranking the function points is very difficult with E-Commerce system. The existing metrics for calculating Complexity Adjustment Factor is not sufficient. It also faces the empirical risks like financial risks, environmental and ecological risks and security risks. Applications/Improvements: At last, it will give the risks associated to the size estimation using function points.Keywords
Agile, COSMIC (Common Software Measurement International Consortium), Delphi Cost Estimation, E-Commerce, Expert Judgment, Function Point (FP), Function Point Analysis (FPA), Line of Codes (LOC)Full Text