Program Guardian: screening system with a novel speaker recognition approach for smart TV

This paper presents Program Guardian, which is a speaker recognition-based screening system for smart TV. The system identifies a specific person from his or her voice such that the smart TV can provide suitable programs for that person. This system is based on a robust speaker recognition system that uses robust principal component analysis (RPCA) and a sparse representation classifier (SRC). First, i-vectors that are generated from supervectors of Gaussian mixture models (GMMs) are used to generate the basic atoms of an over-complete dictionary. The i-vectors are then transformed using RPCA. The SRC is produced from transformed i-vector-based RPCA vectors. Finally, the sparse representation classifier corresponding to the target speaker with the least reconstruction error is constructed. NIST speaker recognition evaluation data base is used in our experiment. The results show that the proposed speaker recognition system is feasible and offers advantages over accuracy.