Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/246056
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dc.date.accessioned2019-06-07T04:39:36Z-
dc.date.available2019-06-07T04:39:36Z-
dc.identifier.urihttp://hdl.handle.net/10603/246056-
dc.description.abstractAutomatic Speech Recognition (ASR) systems have witnessed a lot of progress in the past decade. In high resourced scenarios like English, ASR systems have shown performance comparable to human parity level on specific tasks. ASR systems for Indian languages are less studied compared to other high resourced languages like English. Developing an Indian language ASR systems requires addressing certain challenges which are innate to Indian languages. Often Indian language ASR systems have to be developed for low-resourced scenarios. Apart from multilingual nature, bilingualism is very prevalent in the Indian population which leads frequent code-switching and word borrowing between any two languages. Operating parallel ASR systems with code-switching capabilities in Indian scenarios is a huge challenge. This motivated us to work towards multilingual ASR systems which can handle code-mixing and word borrowing efficiently. In this thesis, we address various issues related to the development of ASR systems for Indian scenarios. An integrated ASR system is developed using common phone-set which can efficiently handle multilingual code-mixed speech. Acoustic modeling approaches such as HMM-GMM, HMM-SGMM and RNN-CTC have been studied to find the most suitable acoustic model. Residual networks have been explored to improve the performance of the joint acoustic models. Studies directed towards supplementing the conventional features along with articulatory features have been explored for developing multilingual ASR systems. Fricative landmarks are detected and the detected landmarks are used as the features for improving the performance of multilingual ASR system. Distinctive features from speech are modeled using a statistical approach and their relevance for improving the performance of a multilingual ASR is explored. In a low resourced scenario, the meta-level information about the speaker is not accessible. A speaker normalization method that can handle those scenarios is explored.
dc.format.extentAll Pages
dc.languageEnglish
dc.relation
dc.rightsself
dc.titleSalient Features for Multilingual Speech Recognition in Indian Scenario
dc.title.alternative
dc.creator.researcherHari Krishna Vydana
dc.subject.keywordAutomatic Speech Recognition
dc.subject.keywordCode-Mixing
dc.subject.keywordCommon Pone-set
dc.subject.keywordEngineering and Technology,Engineering,Engineering Multidisciplinary
dc.subject.keywordIndian Languages
dc.subject.keywordIndian Scenarios
dc.subject.keywordJoint Acoustic Model
dc.subject.keywordMultilingual
dc.subject.keywordResidual Networks
dc.description.note
dc.contributor.guideAnil Kumar Vuppala
dc.publisher.placeHyderabad
dc.publisher.universityInternational Institute of Information Technology, Hyderabad
dc.publisher.institutionElectronics and Communication Engineering
dc.date.registered27/12/2014
dc.date.completed08/01/2019
dc.date.awarded25/05/2019
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Electronic and Communication Engineering

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appendix.pdfAttached File747.31 kBAdobe PDFView/Open
bibliography.pdf133.94 kBAdobe PDFView/Open
chapter1.pdf145.78 kBAdobe PDFView/Open
chapter2.pdf878.15 kBAdobe PDFView/Open
chapter3.pdf222.86 kBAdobe PDFView/Open
chapter4.pdf277.99 kBAdobe PDFView/Open
chapter5.pdf2.27 MBAdobe PDFView/Open
chapter6.pdf163.32 kBAdobe PDFView/Open
chapter7.pdf163.26 kBAdobe PDFView/Open
chapter8.pdf128.7 kBAdobe PDFView/Open
front_pages.pdf321.76 kBAdobe PDFView/Open
publications.pdf118.03 kBAdobe PDFView/Open


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