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Categorization of Hindi phonemes by neural networks

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Abstract

The prime objective of this paper is to conduct phoneme categorization experiments for Indian languages. In this direction a major effort has been made to categorize Hindi phonemes using a time delay neural network (TDNN), and compare the recognition scores with other languages. A total of six neural nets aimed at the major coarse of phonetic classes in Hindi were trained. Evaluation of each net on 350 training tokens and 40 test tokens revealed a 99% recognition rate for vowel classes, 87% for unvoiced stops, 82% for voiced stops, 94.7% for semi vowels, 98.1% for nasals and 96.4% for fricatives. A new feature vector normalisation technique has been proposed to improve the recognition scores.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Department of Training and Technical Education, Ambedkar Institute of Technology, Shakarpur, 110092, Delhi, India

    A. Dev

  2. CSIR Complex, CSIO (Delhi Centre), Pusa Campus, 110012, New Delhi, India

    S. S. Agrawal

  3. Department of Computer Engineering, Delhi College of Engineering, Bawana Road, Samaipur Badli, Delhi, India

    D. R. Choudhury

Authors
  1. A. Dev
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  2. S. S. Agrawal
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  3. D. R. Choudhury
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Correspondence to A. Dev.

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Dev, A., Agrawal, S.S. & Choudhury, D.R. Categorization of Hindi phonemes by neural networks. AI & Soc 17, 375–382 (2003). https://doi.org/10.1007/s00146-003-0263-0

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  • DOI: https://doi.org/10.1007/s00146-003-0263-0

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