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On the application of quantum clustering on speech data

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Abstract

In this work, Quantum clustering (QC) algorithm is applied to a labeled dataset of Arabic vowels. The accuracy and processing time are, then, compared with nonhierarchical kernel approaches for unsupervised clustering; namely, k-means, self-organizing map and fuzzy c-means. The choice of speech data is according to large database statistics which reveal that vowels class represents about 60–70% of Arabic speech whereas the remaining percentage is distributed among other sounds. The analysis features, in this work, are the mel-frequency cepstarl coefficients. The results show that all algorithms are competitive from accuracy point of view while QC still guarantees the solution stability.

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  1. Engineering Mathematics and Physics Department, Faculty of Engineering, Cairo University, Giza, 12613, Egypt

    M. Hesham Farouk

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  1. M. Hesham Farouk
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Farouk, M.H. On the application of quantum clustering on speech data. Int J Speech Technol 20, 891–896 (2017). https://doi.org/10.1007/s10772-017-9458-5

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  • DOI: https://doi.org/10.1007/s10772-017-9458-5

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