Abstract: A speech recognition method, medium, and system. The method includes detecting an energy change of each frame making up signals including speech and non-speech signals, and identifying a speech segment corresponding to frames that include only speech signals from among the frames based on the detected energy change.

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).

Abstract: A signature to be verified is written on an area which carries horizontal and vertical lines using a pen. As the lines are crossed signals representative of the vertical and horizontal components of the pen tip velocity and whether the tip is in contact with the area are passed to a computer. A hidden Markov model derived from vertical and horizontal velocities and a "contact" signal occurring as a number of authentic signatures are written is stored by the computer. A forward pass of the Baum-Welch algorithm is used to calculate the probability that the vertical and horizontal components of the pen tip and the contacts between pen tip and the writing area could have been produced from the hidden Markov model. This probability is used to decide whether the signature is authentic.