Continuous feature learning representation to XGBoost classifier on the aggregation of discriminative Features using DenseNet-121 architecture and ResNet 18 architectures towards Apraxia Recognition in the Child Speech Therapy

Due to the peculiar cell growth, apraxia is one of the common types of speech stuttering seen in youngsters. Machine learning-based architecture has been used to automatically categorize the disfluency based on the feature and its properties, however, these models use more energy, and processing time, and will have worse scalability and dependability. Deep learning architecture has been used to overcome these restrictions as it is more effective and efficient at differentiating speech characteristics. Utilizing ResNet -18 convolutional neural networks and DenseNet-121 convolution network architectures, continuous feature learning representation has been developed as a feature extraction technique. The combined extracted feature is given to the XGBoost Classifier for Apraxia identification. Utilizing Ultra Suite Repository, the suggested model's experimental findings have been assessed in Python. When compared to traditional methods, the suggested model's performance analysis produced 98 percent accuracy, which is evaluated.