Informatics Publishing Limited

Aman Ankit ¹, Sonu Kumar Mishra ¹, Rinaz Shaikh ¹, Chandraketu Kumar Gupta ¹, Prakhar Mathur ¹, Soudamini Pawar ¹, Anil Cherukuri ¹

Affiliations
1 Department of Computer Engineering, Dr. D.Y. Patil College of Engineering, IN

Abstract

Speech recognition or speech to text processing, is a process of recognizing human speech by the computer and converting into text. In speech recognition, transcripts are created by taking recordings of speech as audio and their text transcriptions. Speech based applications which include Natural Language Processing (NLP) techniques are popular and an active area of research. Input to such applications is in natural language and output is obtained in natural language. Speech recognition mostly revolves around three approaches namely Acoustic phonetic approach, Pattern recognition approach and Artificial intelligence approach. Creation of acoustic model requires a large database of speech and training algorithms. The output of an ASR system is recognition and translation of spoken language into text by computers and computerized devices. ASR today finds enormous application in tasks that require human machine interfaces like, voice dialing, and etc. Our key contribution in this paper is to create corpora for Marathi language and explore the use of Sphinx engine for automatic speech recognition.

Keywords

Automatic Speech Recognition (ASR), Speech Analysis, Modeling Techniques, Acoustic-Phonetic Approach, Pattern Recognition Techniques, Language Modeling, Hidden Markov Model, Sphinx Engine, Phonemes, Lexicons.

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Prachi Dalvi ¹, Varsha Mandave ¹, Madhu Gothkhindi ¹, Ankita Patil ¹, S. Kadam ¹, Soudamini Pawar ¹

Affiliations
1 Department of Computer Engineering, D Y Patil College of Engineering, IN

Abstract

Ontology is defined as shared specification of conceptual vocabulary used for formulating knowledge-level theories about a domain of discourse. Dataset is created by manually collecting information about different diseases related to crops. Ontology modeling is used for knowledge representation of various domains. India is an agricultural based economic country. Majority of Indian population relies on farming but the technologies are sparsely used for the aid of farmers. Ontology based modeling for agricultural knowledge can change this scenario. The farmers can understand it easily in their native language. We proposed a system which will model and extract knowledge in Marathi language. In this paper, we review various existing agriculture ontology's along with some of Natural Language Processing (NLP) models. Model ontology for agriculture domain system aims to retrieve relevant answers to the farmer's query. We explored Rule-Based and Conditional Random Fields based models for Ontology extraction. The extraction methods and preprocessing phases of proposed system is discussed.

Keywords

Ontology Modeling, Agriculture, NLP, Marathi, Domain Ontology.

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