We worked on Screening and analysis of specific language impairment in young children by analyzing the textures of the speech signals.We published the paper at the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).As 2nd author, my contribution was processing the audio data, remodeling, and applying various machine learning models to develop a system to screen specific language impairment in young children. We were successful in developing a model with 98% accuracy.

We had hands-on experience with the use of machine learning models with IoT devices. We generated datasets using Raspberry Pi, Pre-processed the Datasets using outlier detection, Normalization, Binarization, Data cleaning, etc., applied different machine-learning algorithms like liner regression, logistic regression, neural network, SVM, Decision Tree, and K-NN classification (using various distance metrics), estimated the quality of predictions using various model evaluation metrics.