Pattern representation model training apparatus

An apparatus for training a pattern representation model for classifying a vector sequence of physical data into one of a plurality of categories, the model including a hidden Markov model for each category having output probability densities defined by a mixture of continuous densities and having a central vector representing the means of the continuous densities, the apparatus comprising:

• means for comparing a training vector sequence of a known category to continuous mixture density hidden Markov models to compute a probability for each of the categories;
• means operative in response to the means for comparing for selecting the hidden Markov model, of a category other than the known category, which provided the maximum probability in response to the training vector sequence;
• and
• means responsive to a selection by the means for selecting for moving, on the basis of the training vector sequence, the central vectors of the selected hidden Markov model and of the hidden Markov model of the known category.

A method for training a continuous density hidden Markov model representation of patterns of physical data, comprising the steps of:

• preparing a plurality of hidden Markov models, each hidden Markov model corresponding to a category and having a central vector indicative of the means of the continuous mixture density;
• computing a probability corresponding to an input pattern of a known category for each prepared hidden Markov model;
• selecting the category of the prepared hidden Markov model which has the maximum probability corresponding to the input pattern;
• moving, when the selected category is not the known category, the central vectors of the hidden Markov models of the known category and of the selected category prepared hidden Markov model.

An apparatus for training a pattern representation model comprised of a plurality of hidden Markov models with continuous mixture densities, each hidden Markov model representing a separate category of pattern of physical data and having a central vector indicative of the means of the continuous mixture density, the apparatus comprising:

• first means for comparing a training pattern of a known category to the plurality of hidden Markov models to obtain a probability for each hidden Markov model;
• means for selecting each hidden Markov model, of a category other than the known category, which provided in response to the training pattern a probability within a predetermined distance of the probability provided by the hidden Markov model of the known category;
• second means, operative in response to a selection by the means for selecting, for comparing the maximum probability provided by the selected hidden Markov model to the probability provided by the hidden Markov model of the known category;
• and
• means, operative in response to the comparison by the second means for comparing, for moving the central vectors of the hidden Markov model of the known category and of the selected hidden Markov model.

An method for training a pattern representation model for classifying and discriminating an acoustic feature vector sequence into one of a plurality of categories, the model including a hidden Markov model for each category having output probability densities defined by a mixture of continuous densities and having a central vector representing the means of the continuous densities, the method comprising the steps of:

• comparing a training acoustic feature vector sequence of a known category to the continuous mixture density hidden Markov models of compute a probability for each of the categories;
• selecting the hidden Markov model, of a category other than the known category, which provided the maximum probability in response to the training acoustic feature vector sequence;
• and
• moving, on the basis of the training acoustic feature vector sequence, the central vectors of the selected hidden Markov model and of the hidden Markov model of the known category.

A method for training a pattern representation model comprised of a plurality of hidden Markov models with continuous mixture densities, each hidden Markov model representing a separate category of pattern of physical data and having a central vector indicative of the means of the continuous mixture density, the method comprising:

• (a) comparing a training pattern of a known category to the plurality of hidden Markov models to obtain a probability for each hidden Markov model;
• (b) selecting each hidden Markov model, of a category other than the known category, which provided in response to the training pattern a probability within a predetermined distance of the probability provided by the hidden Markov model of the known category;
• (c) comparing the maximum probability provided by the selected hidden Markov model to the probability provided by the hidden Markov model of the known category;
• and
• (d) moving the central vectors of the hidden Markov model of the known category and of the selected hidden Markov model on the basis of the step (c) of comparing.