Method for pattern recognition

• US 5,943,438 A
• Filed: 09/08/1997
• Issued: 08/24/1999
• Est. Priority Date: 03/07/1995
• Status: Expired due to Term

• Current Assignee: Unify GmbH & Co. KG (Atos SE)
• Original Assignee: Siemens AG
• Inventors: Zunkler, Klaus
• Primary Examiner(s): Au, Amelia
• Assistant Examiner(s): AHMED, SAMIR ANWAR
• Application Number:
  US08/913,182
• Time in Patent Office: 715 Days
• Field of Search: 382/119, 382/121, 382/159, 704/231, 704/242, 704/256
• US Class Current: 382/159
• CPC Class Codes: G10L 15/142 Hidden Markov Models [HMMs]
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence
• CPC Class Codes: G10L 2015/088 Word spotting

Abstract

A special method recognizes patterns in measurement signals. Speech signals or signals emitted by character recognition apparatuses are thereby meant. For the execution of the invention, the hidden Markov models with which the patterns to be recognized are modeled are expanded by a special state that comprises no emission probability and transition probability. In this way, the temporal position of the sought pattern becomes completely irrelevant for its probability of production. Furthermore, the method offers the advantage that new and unexpected disturbances can also be absorbed without the model'"'"'s having to be trained on them. In contrast to standard methods, no training on background models need be carried out. However, this means a higher expense during the recognition of the patterns, since the individual paths of the Viterbi algorithm have to be normed to the current accumulated probabilities in the path with respect to their probabilities, in order to be able to compare them. The inventive method offers the advantage that only the time segment of the measurement signal also containing the pattern has to be analyzed. An increased probability of a hit is thereby reconciled with a lower computing expense.

Claims

1. A method for recognizing at least one defined pattern modeled by hidden Markov models in a time-variant measurement signal on which at least one disturbing signal is superposed, comprising the steps of:

a) supplementing each of the hidden Markov models at its beginning and at its end by respectively one single identical state that serves to represent the disturbing signal and has at least the following characteristics;

in order to achieve independence of temporal position of defined pattern, said single identical state is free of transition probabilities,

in order to achieve independence of disturbing signals in temporal environment of the defined pattern, said single identical state is free of emission probabilities,

b) seeking and recognizing the defined pattern with a predetermined comparison method using expanded hidden Markov models in a time-variant measurement signal.

2. A method according to claim 1, wherein said predetermined comparison method is the Viterbi algorithm.

3. A method according to claim 1, wherein said predetermined comparison method is the Baum-Welch method.

4. A method according to claim 2, further comprising the step of:

in the comparison method in each recombination of paths, norming overall emission probabilities accumulated per path to a number of the emission probabilities taken into account up to this time, in order to avoid a preferring of shorter paths.

5. A method according to claim 4, wherein said step of norming ensues in the following way:

##EQU5## Segm(l)=Segm(N)=S₀ a_ij is notated as A_ij (i.e., A_ij =const lna_ij)

b_j (O_t) is notated as B(S_k,O_t)=-const ln b_k (O_t))

δ

_t (j) is notated as Δ

_t (j)

t;

current time

j;

current model state

a_ij ;

the transition probability

b_j (O_t);

the emission probability of observation O_t

N;

number of model states

whereby l_t (i) designates the number of Δ

^increment that are accumulated in Δ

_t (i) and that come from the "otherwise" case of the Δ

^increment definition;

scores are notated in place of probabilities.

6. A method according to claim 3, comprising the step of:

performing a summation function in place of a maximum operation.

7. A method according to claim 1, wherein said measurement signal is a speech signal.

8. A method according to claim 1, wherein said measurement signal is a handwriting process signal.

9. A method according to claim 8, wherein said measurement signal is obtained optically.

10. A method according to claim 8, wherein said measurement signal is obtained by measurement of pressure used during writing.