J. Jayabharathy
Editorial Notes
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ABSTRACT
The need for gathering, organizing and archiving information becomes a challenging issue because of the voluminous information especially in the area of TV broadcasts. Since there are many TV talk shows in the form of videos and speeches, documentation becomes a major challenging issue. Categorizing the TV programme content helps to organize the related themes in to a single group. This in turn improves the efficiency of navigation and information retrieval. The rapid growth in number of talk shows in various domains such as news groups, medical talk shows, cooking tips, politics and speeches have led to greater complexity in converting the videos and categorizing the TV talk show programme's efficiently in the form of text. This paper introduces an integrated framework for speech categorization based on clustering which is not addressed in the existing system. A heterogeneous integrated framework and a new clustering algorithm named as Theme based Dynamic Document Clustering (TDDC) for dynamic environment have been proposed. The main goal of the system is to help the users, especially the hearing impaired people to locate and retrieve relevant information in the form of domain specific document for TV talk shows. The videos dataset for various domains obtained from DR.Oz.com are considered for experimental analysis. F measure, Inter and Intra cluster similarity are the performance metrics used for experimental analysis. The proposed TDDC for dynamic environment which is compared with the existing FIS (Frequent Item Set) clustering algorithm and the results are analyzed.
- F. Vallet, S., Essid, J. Carrive, and G. Richard. 2012. TV Content Analysis: Techniques and Applications (Chapter High-Level TV TalkShow Structuring Centered on Speakers' Interventions, CRC, Taylor Francis,.Google Scholar
- K. Jain., M.N. Murty and P.J. Flynn 1999. Data clustering A review, ACM Computing Surveys, Vol. 31, No. 3. Google ScholarDigital Library
- X. Anguera., S. Bozonnet, N. Evans, C. Fredouille, G. Friedland, and O. Vinyals. 2012. Speaker diarization: A review of recent research, IEEE Trans. Audio, Speech, Language Processing., vol. 20, no. 2, pp. 356--370. Google ScholarDigital Library
- Bigot, I.Ferrané., J. Pinquier, and R. André-Obrecht. 2010. Speaker role recognition to help spontaneous conversational speech detection, In the Proceedings of ACM Workshop Searching for Spontaneous Conversational Speech. Google ScholarDigital Library
- Meriem Bendris., DelphineCharlet; Gérard Chollet. 2010. "Talking faces indexing in TV-content", International Workshop on Content-Based Multimedia Indexing (CBMI).Google ScholarCross Ref
- Felicien Vallet, Slim Essid, and Jean Carrive. 2013. A Multimodal approach to speaker diarization on TV talk - shows', IEEE transactions on multimedia, Vol. 15, no. 3. Google ScholarDigital Library
- F. Vallet, S. Essid, J. Carrive, and G. Richard.2010., Robust visual features for the multimodal identification of unregistered speakers, in Proceedings International. Conference Image Processing, Hong Kong, China,Google Scholar
- Xavier Anguera, Simon Bozonnet, Nicholas Evans, Corinne Fredouille; Gerald Friedland; Oriol Vinyals. 2012. Speaker diarization: A review of recent research, IEEE Transactions on Audio, Speech, and Language Processing, Vol 20, No 2, 356--370. Google ScholarDigital Library
- Felicien Vallet, Slim Essid, Jean Carrive and Gael Richard. 2011. High-level TV talk show structuring centered on speakers' interventions", Taylor and Francis.Google Scholar
- H. Salamin and A. Vinciarelli,. 2012. Automatic role recognition in multi-party conversations: An approach based on turn organization, prosody and conditional random fields, IEEE transaction Multimedia, vol. 14, no. 2, pp. 338--345. Google ScholarDigital Library
- Fung, B., Wang, K. and Ester, M. 2003., Hierarchical Document Clustering using Frequent Item Sets, SIAM International Conference on Data Mining, SDM '03.Google Scholar
- Peng, Y., Kou, G., Chen. Z. and Shi, Y. 2006. Recent Trends in Data Mining (DM),: Document Clustering of DM Publications, Proceedings of International Conference on Service Systems and Service Management.Google Scholar
- Peng, Y., Kou, G., Shi, Y. and Chen, Z. 2006. A Hybrid Strategy for Clustering Data Mining Documents, Proceedings of Sixth IEEE International Conference on Data Mining -- Workshop. Google ScholarDigital Library
- Li, Y., Chung, S. and Holt, J. 2008. Text Document Clustering based on Frequent Word Meaning Sequences", Journal of Data & Knowledge Engineering, Elsevier, Vol. 64, No. 1, pp. 381--404. Google ScholarDigital Library
- Blei, D. 2012. Introduction to Probabilistic Topic Models, Communications of the ACM, Vol. 55, No. 4,pp. 77--84. Google ScholarDigital Library
- Jayabharathy. J, Kanmani. S and Sivaranjini. N. 2014. Correlated Concept based Topic Updation Model for Dynamic Corpora, International Journal of Computer Applications, Vol. 89, No.10, pp. 1--7.Google ScholarCross Ref
- Huang, A.. 2008, Similarity Measures for Text Document Clustering, Proceeding of New Zealand Computer Science Student Research Conference, NZCRSC'08.Google Scholar
- Jayabharathy. J and Kanmani. S. 2014. Correlated Concept Based Dynamic Document Clustering Algorithms for Newsgroups and Scientific Literature, Journal on Decision Analytics, Springeropen, Vol. 1, No. 3. pp. 1--21.Google ScholarCross Ref
- Miller, G.A. 1995. WordNet: A Lexical Database for English, Communication of ACM, Vol. 38, No.11, pp. 39--41. Google ScholarDigital Library
- Jayabharathy. J, Kanmani. S and AyeeshaParveen. A. 2011, Document Clustering and Topic Discovery based on Semantic Similarity in Scientific Literature, In the proceedings - Third IEEE International Conference on Communication Software and Networks (ICCSN), China, pp 425--429, 27--29.Google ScholarCross Ref
- Li, F., Zhu, Q. and Lin, X..2009, Topic Discovery in Research Literature Based on Non-negative Matrix Factorization and Testor Theory, Proceedings of Asia-Pacific Conference on Information Processing, pp. 266--269 Google ScholarDigital Library
- Selim Mimaroglu and A. Murat Yagci. 2007 "A Binary Method for Fast Computation of Inter and Intra Cluster Similarities for Combining Multiple Clusterings" In the Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, pp. 452--456. Google ScholarDigital Library
- Steinbach, M., Karypis, G. and Kumar, V. 2000. A Comparison of Document Clustering Techniques, In the Proceedings of Workshop on Text Mining, 6th ACM SIGKDD International Conference on Data Mining (KDD'00), pp. 109--110.Google Scholar
Index Terms
- Integrated Framework for Speech Categorization based on Clustering in Dynamic Environment
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