Automatic Speech Recognition Systems for Regional Languages in India
Ravindra Parshuram Bachate1, Ashok Sharma2

1Ravindra Parshuram Bachate, Research Scholar, School of Computer Science and Engineering, Lovely Professional University, Phagwara (Punjab), India.
2Dr. Ashok Sharma, Associate Professor, School of Computer Science and Engineering, Lovely Professional University, Phagwara (Punjab), India.
Manuscript received on 19 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 585-592 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11080782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1108.0782S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Speech recognition systems has made remarkable progress in last ¬few decades such as Siri, Google assistant, Cortana. For improving the automation in services of all sectors including medical, agriculture, voice dialling, directory services, education, automobile etc., ASR systems must be built for regional languages as most of the Indian population in not familiar with English. Lots of work is done for English language but not for regional languages in India. Developing ASR and ASU systems will change the scenario of current service sector. There are many challenges in building ASR system, Noise reduction is a one of the challenging and still unsolved parameters which affects a lot on performance of any ASR system. Basically, three models required for building any ASR systems- Language model, acoustic model and pronunciation model. In this paper, discussed various parameters affecting on building ASR systems, development of ASR systems, Tools and Techniques used for building an ASR system and research on regional languages ASR system. Deep Neural network (DNN) provides a better way of recognising a speech and accuracy is high.
Keywords: ASR, Acoustic Model, DNN, Language Model, Noise Reduction and Pronunciation Model.
Scope of the Article: Pattern Recognition and Analysis