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Time–domain non-linear feature parameter for consonant classification

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Abstract

This paper introduces an accurate time–domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy.

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Authors and Affiliations

  1. School of Information Science & Technology, Kannur University, Kannur, 670567, Kerala, India

    T. M. Thasleema, P. Prajith & N. K. Narayanan

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  1. T. M. Thasleema
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  2. P. Prajith
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  3. N. K. Narayanan
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Correspondence to T. M. Thasleema.

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Thasleema, T.M., Prajith, P. & Narayanan, N.K. Time–domain non-linear feature parameter for consonant classification. Int J Speech Technol 15, 227–239 (2012). https://doi.org/10.1007/s10772-012-9136-6

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  • DOI: https://doi.org/10.1007/s10772-012-9136-6

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