Authors:
Ireneusz Codello
;
Wiesława Kuniszyk-Jóźkowiak
;
Elżbieta Smołka
and
Adam Kobus
Affiliation:
Institute of Computer Science and Maria Curie-Skłodowska University, Poland
Keyword(s):
Kohonen network, Automatic prolongation recognition, Waveblaster, Neuron reduction, CWT, Continuous wavelet transform, Bark scale.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Speech Recognition
;
Telecommunications
;
Theory and Methods
Abstract:
Automatic disorder recognition in speech can be very helpful for the therapist while monitoring therapy progress of the patients with disordered speech. In this article we focus on prolongations. We analyze the signal using Continuous Wavelet Transform with 22 bark scales, we divide the result into vectors (using windowing) and then we pass such vectors into Kohonen network. We have increased the recognition ratio from 54% to 81% by adding a modification into the network learning process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors’ program – “WaveBlaster”. It is very important that the recognition ratio above 80% was obtained by a fully automatic algorithm (without a teacher). The presented problem is part of our research aimed at creating an automatic prolongation recognition system.