This article explores a method for speaker verification to appropriately set the threshold value used to judge the identities of individual speakers and also considers methods to update speaker models and the robustness of the speaker models so as to make the models more robust with respect to utterance variations, using a small amount of data for updating that was recently uttered. The speaker model is represented by the hidden Markov model (HMM). In the model updating, the parameters of the speaker HMM are estimated from the data for updating and the current parameter values. For setting the threshold, the new threshold value for each speaker is set with the initial value as a value that is passed to an FA rate higher than the equal error rate (a value for which the false rejection rate and the false acceptance rate are equal), which is calculated from the data for updating and which steadily approaches the value that was passed to the equal error rate in concert with updating of the speaker HMM. The results of evaluating this method using text-dependent and text-prompted speaker verification experiments with twenty speakers shows that the average error rate fell by roughly 40% for the text-independent type and by roughly 80% for the text-prompted type as compared to when the model and threshold value are not updated.