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Millions of children and adults suffer from acquired or congenital neuro-motor communication disorders that can affect their speech intelligibility. The automatically characterization of speech impairment can contribute to improve the patient's life quality, and assist experts in assessment and treatment design. In this paper, we present new approaches to improve the analysis and classification of disordered speech. First, we propose an automatic speaker recognition approach especially adapted to identify dysarthric speakers. Secondly, we suggest a method for the automatic assessment of the dysarthria severity level. For this purpose, a model simulating the external, middle and inner parts of the ear is presented. This ear model provides relevant auditory-based cues that are combined with the usual Mel-Frequency Cepstral Coefficients (MFCC) to represent atypical speech utterances. The experiments are carried out by using data of both Nemours and Torgo databases of dysarthric speech. Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs) and hybrid GMM/SVM systems are tested and compared in the context of dysarthric speaker identification and assessment. The experimental results achieve a correct speaker identification rate of 97.2% which can be considered promising for this novel approach; also the existing assessment systems are outperformed with a 93.2% correct classification rate of dysarthria severity levels. [ABSTRACT FROM AUTHOR]

In Non Destructive Testing (NDT) of materials, the ultrasonic waves propagating in a structure are reflected or refracted from the presented discontinuities. The reached waves are received and converted to electrical signals containing informations about the internal defects. The characterization of those defects is an important task and the use of tools of signal processing gives an appreciated help in the decision making for the human operators. This work is a contribution for the defect characterization. The Neural Networks are used in order to classify reached signals allowing to distinguish between signals reflected from two different defects (cracks and inclusions) included in a welding. [ABSTRACT FROM AUTHOR]