In this paper a new text-independent speaker verification method GSMSV is proposed based on likelihood score normalization. In this novel method a global speaker model is established to represent the universal features of speech and normalize the likelihood score. Statistical analysis demonstrates that this normaliza- tion method can remove common factors of speech and bring the differences between speakers into prominence. As a result the equal error rate is decreased significantly, verification procedure is accelerated and system adaptability to speaking speed is improved. In this paper a new text-independent speaker verification method GSMSV is proposed based on likelihood score normalization. In this novel method a global speaker model is established to represent the universal features of speech and normalize the likelihood score. Statistical analysis demonstrates that this normaliza- tion method can remove common factors of speech and bring the differences between speakers into prominence. As a result the equal error rate is decreased significantly, verification procedure is accelerated and system adaptability to speaking speed is improved.