Weighted entropy cortical algorithms for isolated Arabic speech recognition

Cortical algorithms (CA) inspired by and modeled after the human cortex, have shown superior accuracy in few machine learning applications. However, CA have not been extensively implemented for speech recognition applications, in particular the Arabic language. Motivated to apply CA to Arabic speech recognition, we present in this paper an improved CA that is efficiently trained using an entropy-based cost function, and an entropy based weight update rule. We modify the strengthening and inhibiting rules originally employed in CA during feedback training with weighted entropy concepts. Preliminary results show the merit of the proposed modifications in the recognition of isolated Arabic speech and motivate follow on research.