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Virparia, Paresh V.
- Clustering Approach in Context Free Data Cleaning
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National Journal of System and Information Technology, Vol 2, No 1 (2009), Pagination: 83-90Abstract
In this era of Knowledge, organizations can gain competitive advantage only by proficient data analysis. This paper emphasizes on application of clustering in context free data cleaning by correcting values of attributes, using various sequence similarity metrics, where reference data set is not available, to improve the quality of data which in turn lead to eminent data analysis. Authors propose an algorithm to examine suitability of value to correct other values of attributes. Various sequence similarity metrics were used, to find distance of two values of attributes, to test the data and generate results. Experimental results show how the approach can effectively clean the data without reference data.Keywords
Clustering, Context Free Data Cleaning, Sequence Similarity Metrics
References
- Hui Xiong, Gaurav Pandey, Michael Steinbach, Vipin Kumar “Enhancing Data Analysis with Noise Removal” in IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 3, pp. 304-319, March 2006.
- Lukasz Ciszak “Application of Clustering and Association Methods in Data Cleaning”, in Proc. of Int. Multiconference on Computer Science and Information Technology, Vol. 3, pp. 97-103, 2008.
- Sohil D Pandya, Dr. Paresh V Virparia “Data Cleaning in Knowledge Discovery in Databases: Various Approaches”, in Proc. of National Seminar on Current Trends in IT (CTICT) – 2009, February 2009.
- W Cohen, P Ravikumar, S Fienberg “A Comparison of String Distance Metrics for Name-Matching Tasks” in Proc. of the IJCAI-2003
- http://en.wikipedia.org/
- http://www. dcs.shef.ac.uk/~sam/simmetric.html
- Gujarati Language Speech Recognition System for Identifying Smartphone Operation Commands
Abstract Views :495 |
PDF Views:2
Authors
Affiliations
1 Dept. of Computer Science, Sardar Patel University, IN
1 Dept. of Computer Science, Sardar Patel University, IN
Source
National Journal of System and Information Technology, Vol 8, No 2 (2015), Pagination: 79-87Abstract
Natural Language Processing provides the facility of operating a system with speech and the system can interact with the user accordingly. Many speech recognition applications also provide the support for regional languages. In this paper, we would like to discuss the work that will add some facility to the state-of-the-art facility of speech recognition of Gujarati language. We have designed and developed a system that allows the users to give commands to their smartphone in Gujarati language for some basic facilities like calling, sending SMS, etc. The vocabulary includes total of 60 words consisting of Gujarati digits, some persons' name to be considered as a contact name and operational commands which yield the overall average recognition accuracy of 82.23%.Keywords
Speech Recognition, Phoneme, Hidden Markov Model (HMM), Java Grammar, Lexeme.References
- Jurafsky, Martin – “Speech and Language Processing”, Pearson, 2000
- Gunnar Fant - “Speech Acoustics and Phonetics: Selected Writings” [e-book], Springer, 2006
- Hinrich Schtze - “Foundations of statistical natural language processing” [e-book], MIT Press,1999
- Ms. Jigisha Patel, Mr. Pritesh N. Patel, Dr. P. V. Virparia – “Acoustic and Phonetic Confusions in Accented Gujarati Speech Recognition” : National Journal Of Engineering Science And Management ( ISSN 2249 -0264) Bhopal
- Ms. Jigisha Patel, Mr. Pritesh N. Patel, Dr. P. V. Virparia – “Voice Enabled Telephony Commands Using Gujarati Speech Recognition” : International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X, Volume 3, Issue 10, October 2013, [Impact Factor: 2.080, Indexed]
- Ms. Jigisha Patel, Mr. Pritesh N. Patel - “Dialectical issues in speech recognition for Gujarati language”, in the Proceedings of the National Conference on Advances in Computing-2011, North Maharashtra University
- http://cmusphinx.sourceforge.net/sphinx4/#what_is_sphinx4
- http://cmusphinx.sourceforge.net/wiki/tutorialoverview
- http://www.speech.cs.cmu.edu/tools/lmtool-new.html
- http://cmusphinx.sourceforge.net/sphinx4/#architecture_and_api1
- http://cmusphinx.sourceforge.net/sphinx4/#download_and_install
- https://javacc.java.net/doc/javaccgrm.html
- http://cmusphinx.sourceforge.net/wiki/sphinx4:jsgfsupport
- http://cmusphinx.sourceforge.net/sphinx4/javadoc/edu/cmu/sphinx/jsgf/JSGFGrammar.html
- http://www.w3.org/TR/speech-grammar/
- http://docs.oracle.com/cd/E17802_01/products/products/java-media/speech/forDevelopers/jsapi-doc/javax/speech/recognition/RuleGrammar.html
- http://www.ling.helsinki.fi/kit/2004s/ctl310gen/L7-Speech/JSAPI/Recognition.html
- http://cmusphinx.sourceforge.net/doc/sphinx4/edu/cmu/sphinx/jsgf/JSGFGrammar.html
- http://www.w3.org/TR/jsgf/
- http://oxygen.lcs.mit.edu/Speech.html
- http://support.docsoft.com/help/whitepaper-asr.pdf