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Neural Network on the Performance of Bangla Automatic Speech Recognition

Qamrun Nahar Eity, Md. Khairul Hasan, G. M. Monjur Morshed Mrida, Mohammad Nurul Huda in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2019
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Qamrun Nahar Eity, Md. Khairul Hasan, G. M. Monjur Morshed Mrida, Mohammad Nurul Huda
10.5120/ijais2019451822
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  1. Qamrun Nahar Eity, Md. Khairul Hasan, Monjur Morshed G M Mrida and Mohammad Nurul Huda. Neural Network on the Performance of Bangla Automatic Speech Recognition. International Journal of Applied Information Systems 12(24):7-11, October 2019. URL, DOI BibTeX

    @article{10.5120/ijais2019451822,
    	author = "Qamrun Nahar Eity and Md. Khairul Hasan and G. M. Monjur Morshed Mrida and Mohammad Nurul Huda",
    	title = "Neural Network on the Performance of Bangla Automatic Speech Recognition",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "October, 2019",
    	volume = 12,
    	number = 24,
    	month = "October",
    	year = 2019,
    	issn = "2249-0868",
    	pages = "7-11",
    	url = "http://www.ijais.org/archives/volume12/number24/1065-2019451822",
    	doi = "10.5120/ijais2019451822",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

In this paper, the performance of different Bangla (widely used as Bengali) Automatic Speech Recognition (ASR) systems based on local features (LFs) to observe the effects of multilayer neural network (MLN) on it, is evaluated. These ASR systems use 3000 sentences uttered by 30 speakers from a wide area of Bangladesh, where Bangla is used as a native language. In the experiments, at first LFs are extracted from the input speech and these LFs are inputed into a multilayer neural network (MLN) for obtaining phoneme probabilities for all the Bengali phonemes considered in this study. Then, these phoneme probabilities are modified by taking logarithm or normal values, and either of these values are inputted to the hidden Markov model (HMM) based classifier to obtain word corrrect rate (WCR), word accuracy(WA) and sentence correct rate (SCR). From this study, it is observed that the ASR method which incorporates an MLN in its arechitecture improves the word recognition accuracy with fewer components in HMMs.

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Keywords

Local features; multi layer neural network; boost up; logarithm; normalization; hidden Markov model; automatic speech recognition