Abstract
Classification of speech signals in a time domain can be made through a clustering process of time windows into which examined speech signals are divided. Disturbances in speech signals of patients having some problems with the voice organ cause some difficulties in formation of coherent clusters of similar time windows. A quality of a clustering process result can be used as an indicator of non-natural disturbances in articulation of selected phonemes by patients. In the paper, we describe a procedure based on this fact. A special ant based algorithm is used to cluster time windows being time series. In this algorithm, a new local function, formulas for picking and dropping decisions as well as some additional operations are implemented to adjust the clustering process to a classification ability.
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Pancerz, K., Lewicki, A., Tadeusiewicz, R., Szkoła, J. (2012). Classification of Speech Signals through Ant Based Clustering of Time Series. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_35
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DOI: https://doi.org/10.1007/978-3-642-34630-9_35
Publisher Name: Springer, Berlin, Heidelberg
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