Method for recognizing patterns in time-variant measurement signals

A method for recognizing patterns in time-variant measurement signals by classifying a temporal sequence of feature vectors and reclassification in pairs, comprising the steps of:

• segmenting the sequence of feature vectors which is to be classified using a Viterbi decoding algorithm, this sequence to be classified being compared with a set of hidden Markov models;
• calculating, for pairs of hidden Markov models, modified total emission probabilities for each hidden Markov model of a respective pair of the hidden Markov models on a precondition that a respective other hidden Markov model of a same pair competes with the hidden Markov model under review, the total emission probability being calculated, for generating the sequence to be classified by a hidden Markov model, by calculating for all feature vectors of the sequence to be classified and for all states of the hidden Markov model a local logarithmic emission probability for generating the respective feature vector by the respective state, and by calculating an accumulated logarithmic emission probability for each state as a sum of its local logarithmic emission probability and an accumulated logarithmic emission probability of its best possible predecessor state, the best possible predecessor state being logged;
• determining a respective more probable hidden Markov model of said respective pair;
• selecting the hidden Markov model having the highest total emission probability from among all pairs under review;
• and
• determining an optimum assignment path from feature vectors to states of the hidden Markov models by backtracking.

A method for recognizing patterns in time-variant measurement signals by classifying a temporal sequence of feature vectors and reclassification in pairs, comprising the steps of:

• segmenting the sequence of feature vectors which is to be classified using a Viterbi decoding algorithm, this sequence to be classified being compared with a set of hidden Markov models;
• calculating for each hidden Markov model a total emission probability for the generation of the sequence to be classified by this hidden Markov model;
• determining an optimum assignment path from feature vectors to states of the hidden Markov models by backtracking;
• calculating, for at least one pair of hidden Markov models, modified total emission probabilities for each hidden Markov model of said at least one pair on a precondition that a respective other hidden Markov model of a same pair competes with the hidden Markov model under review, a local logarithmic modified emission probability being calculated for all feature vectors of the temporal sequence for generating a respective feature vector by the corresponding state of the respective hidden Markov model by adding local logarithmic modified emission probabilities recursively along the already calculated assignment path to an accumulated logarithmic modified emission probability, a total emission probability being calculated, for generating the sequence to be classified by a hidden Markov model, by calculating for all feature vectors of the sequence to be classified and for all states of the hidden Markov model a local logarithmic emission probability for generating the respective feature vector by the respective state, and by calculating an accumulated logarithmic emission probability for each state as a sum of its local logarithmic emission probability and an accumulated logarithmic emission probability of its best possible predecessor state, the best possible predecessor state being logged;
• determining a respective more probable hidden Markov model of said at least one pair;
• and
• selecting the hidden Markov model having the highest total emission probability from among all pairs under review.