Efficient score normalization for speaker recognition

Score normalization is an important component in most speech classification tasks including speaker recognition. State-of-the-art scoring approaches use both T-norm and Z-norm. This paper addresses the following goals: better understanding of existing score normalization methods, reducing the need for explicit score normalization, and improving the computational efficiency of score normalization. In addition, the importance of score normalization for speaker identification is demonstrated, and accuracy is improved considerably using various normalization techniques.