Classification of speech signals through ant based clustering of time series

Authors:
Krzysztof Pancerz;Arkadiusz Lewicki;Ryszard Tadeusiewicz;Jarosław Szkoła
Affiliations:
University of Information Technology and Management, Rzeszów, Poland;University of Information Technology and Management, Rzeszów, Poland;AGH University of Science and Technology, Krakow, Poland;University of Information Technology and Management, Rzeszów, Poland
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Year:
2012

Citing 7
Cited 0

The dynamics of collective sorting robot-like ants and ant-like robots

Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants

SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Ant-Based Clustering and Topographic Mapping

Artificial Life
Data Mining: A Knowledge Discovery Approach

Data Mining: A Knowledge Discovery Approach
Clinical Decision Support: The Road Ahead

Clinical Decision Support: The Road Ahead
Ant based clustering of time series discrete data --- a rough set approach

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study

Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing

Quantified Score

Hi-index	0.00

Visualization

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.