• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2017, Volume: 10, Issue: 35, Pages: 1-6

Original Article

Web Recognition of Spoken Hindi


Technology has evolved and computers have but still Indian communities are far from the use of computers, only 37% [13] user of Indian society like persons from academics, health, engineering and research make use of the benefits of computer. Although there is a revolution in development of operating systems in the past two years, there are no operating systems that support Indian languages like Hindi, they only support English language and people who know English can work on these. This work shows the development of speaker independent ASR system for Indian speaker of a particular type i.e. spoken Hindi. Objective: An automated system is developed which accept Hindi query from the user and search the keywords from the query on web and shows the result accordingly. A bilingual lexicon of 3145 words was built. The techniques to convert acoustic speech signals into strings of words were also designed. The dictionary maintains the POS of source to target language and CFG grammar based model for Hindi language was developed. Methods: A speech database consisting of speech files is created at the rate of 16 KHz. Interviews were conducted uto record speech files from users for developing the speech database for training the system. The system takes spoken Hindi as input and generates Hindi sentences. Hindi sentences are parsed with the help of Top-Down Paser and identify the keywords and form a exact query with the help of grammar rules which are given as input to Google search engine to search the result as per query and display on screen. The lexical categories of Hindi language in grammar are done on the bases of verb, noun, preposition, adjective, adverb phrases. Result: A total of 101 speakers from Faridabad district, comprising of both males and females took part in data collection process. The system was tested in a sound proof room and achieved 88.05% accuracy. It is observed that utilization of more training data together with detailed modeling of speech signals can improve the system performance to a level adequate for actual deployment. Application/Improvements: This system provides access to a computer system or to digital content over the internet to illiterate, vision-impaired, urban and semi-urban Indian people who are not able to read/write English language. The same speech interface can be enhanced to work in other regional languages of India like Punjabi, Marathi and Telugu, etc. To enhance or to extend it for other languages, the transfer or translation rules of grammar is needed, which can be generated with great ease by using the same dataset with different target languages. 

Keywords: Corpus, Hindi, Recognition, Speech, Web 


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