A Low-Complexity Speaker-and-Word Recognition Application for Resource-Constrained Devices

We present a low-complexity solution for performing speaker-and-word recognition and demonstrate its suitability for resource-constrained embedded / mobile devices. In the proposed approach, modeling and recognition of speakers and words are performed using Gaussian Mixture Model (GMM), which has relatively low computational complexity. The inability of GMM to capture the temporal information of speech, which is vital for word recognition, has been overcome through a simple, yet effective adaptation. After evaluating the performance of two alternative architectures, an integrated speaker-and-word recognition system based on text-dependent speaker recognition has been proposed. The system has been ported to a mobile device as an Android application and tested in real-life environment.