Text–Independent Speaker Recognition Using Two–Dimensional Information Entropy

Speaker recognition is the process of automatically recognizing who is speaking on the basis of speaker specific characteristics included in the speech signal. These speaker specific characteristics are called features. Over the past decades, extensive research has been carried out on various possible speech signal features obtained from signal in time or frequency domain. The objective of this paper is to introduce two-dimensional information entropy as a new text-independent speaker recognition feature. Computations are performed in time domain with real numbers exclusively. Experimental results show that the two-dimensional information entropy is a speaker specific characteristic, useful for speaker recognition.