Abstract: Speaker verification is important in such applications as financial transactions which are to be carried out automatically by telephone. False acceptances of a speaker cause serious problems but so do frequent false rejections in view of the annoyance caused. Some of the problems of speaker verification are reduced in the invention by forming Hidden Markov Models (HMMs) for each of a number of words using features of utterances of these words from a large number of speakers. These models are known as world models. In addition, for every person whose speech is to be recognized, one HMM is formed for each of the words as uttered by that person. These models are known as personal models. In verification, a person is prompted to repeat a string of isolated or connected words (15) and features from each of these words are extracted (16).