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Continuous Hindi Speech Recognition Using Kaldi ASR Based on Deep Neural Network

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Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

Abstract

Today, deep learning is one of the most reliable and technically equipped approaches for developing more accurate speech recognition model and natural language processing (NLP). In this paper, we propose Context-Dependent Deep Neural-network HMMs (CD-DNN-HMM) for large vocabulary Hindi speech using Kaldi automatic speech recognition toolkit. Experiments on AMUAV database demonstrate that CD-DNN-HMMs outperform the conventional CD-GMM-HMMs model and provide the improvement in word error rate of 3.1% over conventional triphone model.

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Acknowledgements

The authors would like to acknowledge Institution of Electronics and Telecommunication Engineers (IETE) for sponsoring the research fellowship during this period of research.

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Authors and Affiliations

  1. Department of Electronics, Aligarh Muslim University, Aligarh, 202002, Uttar Pradesh, India

    Prashant Upadhyaya, Omar Farooq, Yash Vardhan Varshney & Musiur Raza Abidi

  2. Electrical Engineering, Indian Institute of Science Bangalore, Bengaluru, 560012, Karnataka, India

    Sanjeev Kumar Mittal

Authors
  1. Prashant Upadhyaya
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  2. Sanjeev Kumar Mittal
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  3. Omar Farooq
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  4. Yash Vardhan Varshney
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  5. Musiur Raza Abidi
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Corresponding author

Correspondence to Prashant Upadhyaya .

Editor information

Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

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Upadhyaya, P., Mittal, S.K., Farooq, O., Varshney, Y.V., Abidi, M.R. (2019). Continuous Hindi Speech Recognition Using Kaldi ASR Based on Deep Neural Network. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_26

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