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    End-to-End Speech Recognition of Tamil Language

    Mohamed Hashim Changrampadi1,*, A. Shahina2, M. Badri Narayanan2, A. Nayeemulla Khan3

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1309-1323, 2022, DOI:10.32604/iasc.2022.022021

    Abstract Research in speech recognition is progressing with numerous state-of-the-art results in recent times. However, relatively fewer research is being carried out in Automatic Speech Recognition (ASR) for languages with low resources. We present a method to develop speech recognition model with minimal resources using Mozilla DeepSpeech architecture. We have utilized freely available online computational resources for training, enabling similar approaches to be carried out for research in a low-resourced languages in a financially constrained environments. We also present novel ways to build an efficient language model from publicly available web resources to improve accuracy in More >

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