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Speech nonfluency detection using Kohonen networks

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Abstract

This work covers the problem of application of neural networks to recognition and categorization of non-fluent and fluent utterance records. Fifty-five 4-s speech samples where the blockade on plosives (p, b, t, d, k and g) occurred and 55 recordings of speech of fluent speakers containing the same fragments were applied. Two Kohonen networks were used. The purpose of the first network was to reduce the dimension of the vector describing the input signals. A result of the analysis was the output matrix consisting of the neurons winning in a particular time frame. This matrix was taken as an input for the next self-organizing map network. Various types of Kohonen networks were examined with respect to their ability to classify utterances correctly into two, non-fluent and fluent, groups. Good examination results were accomplished and classification correctness exceeded 76%.

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Authors and Affiliations

  1. Department of Physics, Agricultural University, Akademicka 13, 20-950, Lublin, Poland

    Izabela Szczurowska

  2. Laboratory of Biocybernetics, Institute of Computer Science, Maria Curie-Sklodowska University, pl. Maria Curie-Sklodowska 1, 20-031, Lublin, Poland

    Wiesława Kuniszyk-Jóźkowiak & Elżbieta Smołka

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  1. Izabela Szczurowska
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  2. Wiesława Kuniszyk-Jóźkowiak
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  3. Elżbieta Smołka
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Correspondence to Wiesława Kuniszyk-Jóźkowiak.

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Szczurowska, I., Kuniszyk-Jóźkowiak, W. & Smołka, E. Speech nonfluency detection using Kohonen networks. Neural Comput & Applic 18, 677–687 (2009). https://doi.org/10.1007/s00521-009-0261-3

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  • DOI: https://doi.org/10.1007/s00521-009-0261-3

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