A combined neural network and hidden Markov model approach to speaker recognition

Abstract

Presents a combination approach to text-independent speaker identification. The approach makes use of the strong classification power of an artificial neural network and the hidden Markov model's ability to handle the sequential character of speech. The combination approach is superior to both the neural network approach and the hidden Markov model approach in identification accuracy and computational complexity. >