Classical and Deep Learning Methods for Speech Command Recognition

As an application area of speech command recognition, smart home has provided people a convenient way to communicate with various digital devices. In this study, we aim to investigate both machine learning and deep learning architectures for improved speaker-independent speech command recognition. First, we extract statistical MFCCs vectors to train classical machine learning models: KNN, SVM, and RF. Second, we trained deep learning models using two end-to-end architectures with different inputs. Experimental results indicate that our presented method achieved the highest accuracy and F1 score of 0.846 +/- 0.148 and 0.84 +/- 0.157 on the private dataset.