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Resource Creation for Sanskrit ASR (Automatic Speech Recognition)


Affiliations
1 School of Sanskrit and Indic Studies, 2Jawaharlal Nehru University, New Delhi, India., India
 

There are a few works on Automatic Speech Recognition (ASR) for Sanskrit. Generally, developing an ASR system is a time and cost-consuming multi-layered teamwork. An average of basic 90 to 100 hours of annotated speech corpus is required for the development of a basic ASR system. The present paper is part of a doctoral research work that proposes corpora creation to provide a reliable annotated speech dataset for future research on Sanskrit ASR.

Keywords

Resource Creation, Automatic Speech Recognition for Sanskrit, Indian Language, Sanskrit Manuscripts, Transcription.
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  • Jha, G. N. (2010). Sanskrit Computational Linguistics (Vol. 6465).

  • Bharati, A., Chaitanya, V., & Sangal, R. (2010). Natural Language Processing. New Delhi: PHI Learning Private Limited.

  • Bhattacharya, K. (2006). On the Language of Navya-Nyāya: an Experiment with Precision through a Natural Language. The Journal of Indian Philosophy, IIIIV(1/2), 5-13.

  • Bhattacharya, S. (1990). Some Features of the Technical Language of Navya-Nyāya. Philosophy East and West, XX(2), 129-149.

  • Brigg, R. (1985). Knowledge Representation in Sanskrit and Artificial Intelligence. AI Magazine, 32-39.

  • Deshpande, M. (1991). Prototypes in Pāṇinian Syntax. Journal of the American Oriental Society, III(3), 465-480.

  • Huet, G. (2016). Sanskrit signs and Pāṇinian Scripts. Sanskrit Computational Linguistics, 53-76. New Delhi: D.K. Publishers.

  • Kadvany, J. (2016). Pāṇini's Grammar and Modern Computation. History and Philosophy of Logic, 325-346.

  • Kiparsky, P. (2002). On the Architecture of Pāṇini's Grammar. Hydrabad: Central Institute of English and Foreign Languages.

  • Kuhn, T. (2013). A Principled Approach to Grammars for Controlled Natural Languages and Predictive Editors. Journal of Logic, Language, and Information,XXII(3), 33-70.

  • Kuhn, T. (2014). A Survey and Classification of Controlled Natural Languages. Computational Linguistics,XL(1).

  • Parr, T. (2013). The Definitive ANTLR 4 Reference. Texas: Pragmatic Bookshelf.

  • Sharma, R. N. (2002). The AD of Panini (2nd ed. Vol. 1). New Delhi: MunshiramManoharlal Publishers.

  • Williams, M. (1872). A Sanskṛit-English dictionary etymologically and philologically arranged: With special reference to Greek, Latin, Gothic, German, Anglo-Saxon, and other cognate IndoEuropean languages.Oxford: The Clarendon Press.


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  • Resource Creation for Sanskrit ASR (Automatic Speech Recognition)

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Authors

Devendr Kumar
School of Sanskrit and Indic Studies, 2Jawaharlal Nehru University, New Delhi, India., India
Girish Nath Jha
School of Sanskrit and Indic Studies, 2Jawaharlal Nehru University, New Delhi, India., India

Abstract


There are a few works on Automatic Speech Recognition (ASR) for Sanskrit. Generally, developing an ASR system is a time and cost-consuming multi-layered teamwork. An average of basic 90 to 100 hours of annotated speech corpus is required for the development of a basic ASR system. The present paper is part of a doctoral research work that proposes corpora creation to provide a reliable annotated speech dataset for future research on Sanskrit ASR.

Keywords


Resource Creation, Automatic Speech Recognition for Sanskrit, Indian Language, Sanskrit Manuscripts, Transcription.

References