A Review of Various Score Normalization Techniques for Speaker Identification System

This paper presents an overview of a state-of-the-art text-independent speaker verification system using score normalization. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Normalization of scores is then explained, as this is a very important step to deal with real-world data. When acoustic and prosodic based systems are established, it is advantageous to normalize the dynamic ranges of the score dimensions, that is, likelihood scores from different quality of acousticand prosodic based models. Score normalization methods, linear scaling to unit range and linear scaling to unit variance, are applied to transform the output scores using the background instances so as to obtain meaningful comparison between speaker models. In this fusion system based on linear score weighting approach, the performance of speaker identification is further improved when incorporating prosodic level of information. The evaluation of a speaker verification system is then detailed, and the detection error trade-off (DET) curve is explained.. Then, some applications of speaker verification are proposed, including won-site applications, remote applications, applications relative to structuring audio information, and games.