Abstract: A speech training aid compares a trainee's speech with models of speech, stored as sub-acoustic word models, and a general speech model to give an indication of whether or not the trainee has spoken correctly. An indication of how well the word has been pronounced may also be given. An adult operator enters the word to be tested into the training aid which then forms a model of that word from the stored sub-word speech models. The stored acoustic models are formed by first recording a plurality of words by a plurality of trainees from a given list of single words. These recordings are then processed off-line to give a basic acoustic model of an acceptable or correct sound for each phoneme in the context of the pre-and proceeding phonemes. The acoustic models are Hidden Markov Models. The limits of acceptable pronunciation, applied to different words and trainees, may be adjusted by variable penalty values applied in association with the general speech acoustic model.

Abstract: A children's speech training aid compares a child's speech with models of speech, stored as sub-word acoustic models, and a general speech model to give an indication of whether or not the child has spoken correctly. An indication of how well the word has been pronounced may also be given. An adult operator enters the word to be tested into the training aid which then forms a model of that word from the stored sub-word speech models. The stored acoustic models are formed by first recording a plurality of words by a plurality of children from a given list of single words. These recordings are then processed off-line to give a basic acoustic model of an acceptable or correct sound for each phoneme in the context of the pre- and proceeding phonemes. The acoustic models are Hidden Markov Models. The limits of acceptable pronunciation applied to different words and children may be adjusted by variable penalty values applied in association with the general speech acoustic model.

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.