Efficient Method for Isolated Marathi Digits Recognition using DWT and Soft Computing Techniques

Narkhede, Atul D.; Nemade, Milind U.

doi:10.1109/iot-siu.2018.8519893

Cited by 2 publications

(1 citation statement)

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“…Surrounding and noise makes speech recognition system complicated as it affects a lot on performance and accuracy of the Automatic Speech Recognition System. Speech Recognition accuracy is used for performance measurement of speech recognition system [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic Speech Recognition Systems for Regional Languages in India

2019

ijrte

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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.

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Section: Introductionmentioning

confidence: 99%