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Automatic Segmentation of Spoken Word Signals into Letters Based on Amplitude Variation for Speech to Text Transcription

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Information Systems Design and Intelligent Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 340))

Abstract

In this paper a technique for automatic segmentation of spoken word signals is presented for identifying letters for transcription into textual form. Signal patterns for each letter present in different words have been used for the purpose. Voice signals are obtained by taking pronunciations of 1,000 words available in the standard dictionary. After collecting the signals, pre-processing is performed to reduce the noise taking a heuristically determined threshold value. Then the signals are segmented based on Amplitude Variation (AV) in different portions of the signal, each corresponding to an alphabet in that particular word. Signal Peak Value (SPV) is the feature used for recognizing the letters. Accuracy of the method is estimated using Bagging, Bayes Net, J48, Naive Bayes, PART and SVM classifiers available in Weka. The best and the average classification accuracies obtained in this method are 95.15 % (given by J48 classifier) and 86.92 %, respectively, which are quite acceptable.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, West Bengal University of Technology, BF-142, Salt Lake, Kolkata, 700064, West Bengal, India

    Anik Roy & Santanu Phadikar

Authors
  1. Anik Roy
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  2. Santanu Phadikar
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Correspondence to Anik Roy .

Editor information

Editors and Affiliations

  1. University of Kalyanai, Kalyanai, West Bengal, India

    J. K. Mandal

  2. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  3. Dean, Faculty of Engineering, Technology, University of Kalyani, Kalyani, West Bengal, India

    Manas Kumar Sanyal

  4. Engineering and Technological Studies, University of Kalyani, Kalyani, West Bengal, India

    Partha Pratim Sarkar

  5. Department Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Anirban Mukhopadhyay

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Roy, A., Phadikar, S. (2015). Automatic Segmentation of Spoken Word Signals into Letters Based on Amplitude Variation for Speech to Text Transcription. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 340. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2247-7_63

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  • DOI: https://doi.org/10.1007/978-81-322-2247-7_63

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2246-0

  • Online ISBN: 978-81-322-2247-7

  • eBook Packages: EngineeringEngineering (R0)

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