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Robust Feature Extraction and Recognition Model for Automatic Speech Recognition System on News Report Dataset

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Information and Communication Technology for Competitive Strategies (ICTCS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 400))

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Abstract

Information processing has become ubiquitous. The process of deriving speech from transcription is known as automatic speech recognition systems. In recent days, most of the real-time applications such as home computer systems, mobile telephones, and various public and private telephony services have been deployed with automatic speech recognition (ASR) systems. Inspired by commercial speech recognition technologies, the study on automatic speech recognition (ASR) systems has developed an immense interest among the researchers. This paper is an enhancement of convolution neural networks (CNNs) via a robust feature extraction model and intelligent recognition systems. First, the news report dataset is collected from a public repository. The collected dataset is subjective to different noises that are preprocessed by min–max normalization. The normalization technique linearly transforms the data into an understandable form. Then, the best sequence of words, corresponding to the audio based on the acoustic and language model, undergoes feature extraction using Mel-frequency Cepstral Coefficients (MFCCs). The transformed features are then fed into convolutional neural networks. Hidden layers perform limited iterations to get robust recognition systems. Experimental results have proved better accuracy of 96.17% than existing ANN.

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Author information

Authors and Affiliations

  1. Department of Electronics Engineering, JCBoseUST, Faridabad, India

    Sunanda Mendiratta & Neelam Turk

  2. GB Pant Okhla Campus 1, Delhi Skill & Entrepreneurship University, Delhi, India

    Dipali Bansal

Authors
  1. Sunanda Mendiratta
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  2. Neelam Turk
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  3. Dipali Bansal
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Corresponding author

Correspondence to Sunanda Mendiratta .

Editor information

Editors and Affiliations

  1. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

  2. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  3. University of Peradeniya, Kandy, Sri Lanka

    Roshan G. Ragel

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mendiratta, S., Turk, N., Bansal, D. (2023). Robust Feature Extraction and Recognition Model for Automatic Speech Recognition System on News Report Dataset. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2021). Lecture Notes in Networks and Systems, vol 400. Springer, Singapore. https://doi.org/10.1007/978-981-19-0095-2_56

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  • DOI: https://doi.org/10.1007/978-981-19-0095-2_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0094-5

  • Online ISBN: 978-981-19-0095-2

  • eBook Packages: EngineeringEngineering (R0)

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