| dc.description.abstract | Vocal communication is the most important part of any individual�s life to convey their needs. Right from the first cry of neonates to the matured adult speech, required proper brain co-ordination. Any kind of lack in coordination between brain and speech producing system leads to pathology. Asphyxia, asthma, Sudden Death Syndrome, Deaf (SIDS), etc. are some of teh infant cry pathologies and neuromotor speech disorders such as Dysarthria, Parkinson�s Disease, Cere- bal Palsy, etc. are some of the adult speech-related pathologies. These pathologies lead to damaged or paralysed articulatory movements in speech production and rendering unintelligible words. Infants as well as adults suffering from any of the pathologies face difficulties in conveying the emotions. The infant cry classification and analysis is a highly non invasive method for identifying the reason behind the crying. The present work in this thesis is directed towards analysing and classifying the normal vs. pathological cries using signal processing approaches. Various signal processing methods, such as Constant Q Transform (CQT), Heisenberg�s Uncertainty Principle (U-Vector) and Teager Energy Operator (TEO) are analysed in this thesis. Spectrographic analysis using ten different cry modes in a cry signal is also analysed in this work. In addition to this, an attempt has also been made to analyse various pathologies using the form invariance property of the CQT. In addition to the infant cry analysis, classification of normal vs. pathological cries using 10 fold cross validation on Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) have been adopted. In recent the years, dysarthria has also become one of the major speech technology issue for models, such as Automatic Speech Recognition systems. Dysarthric severity-level classification, has gained immense attention via researchers in the recent years. The dysarthric severity level classification aids in knowing the advancement of the disease, and it�s treatment. In this thesis, the dysarthric speech has been analysed using various signal processing operators, such as TEO, and Linear Energy Operator (LEO) for four different dysarthric severity level against normal speech. With increasing use of artificial intelligence, there has been a significant increase in the use of deep learn- ing methods for pattern classification task. To that effect, the severity level classifi- cation of dysarthric speech, deep learning techniques, such as Convolutional Neural Network (CNN), Light CNN (LCNN), and Residual Neural Network (ResNet) have been adopted. Finally, the performance of various signal processing-based feature has been measured using various performance evaluation methods, such as F1-Score, J-Statics, Matthew�s Correlation Coefficient (MCC), Jaccard�s Index, Hamming Loss, Linear Discriminant Analysis (LDA), and latency period for the better practical deployment of the system. | |
