Method and system for user authentication based on speech recognition and knowledge questions

• US 20070219792A1
• Filed: 03/20/2006
• Published: 09/20/2007
• Est. Priority Date: 03/20/2006
• Status: Abandoned Application

• Current Assignee: Nü Echo, Inc.
• Original Assignee: Nü Echo, Inc.
• Inventors: Normandin, Yves
• Application Number:
  US11/385,228
• Publication Number:
  US 20070219792A1
• Time in Patent Office: Days
• US Class Current: 704/239
• CPC Class Codes: G07C 9/37 using biometric data, e.g. fingerprints, iris scans or voice recognition
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 17/24 the user being prompted to utter a password or a predefined phrase

Abstract

A method and system for user authentication based on speech recognition and knowledge questions. The method comprises receiving a speech recognition result derived from ASR processing of a received utterance. A reference information element is obtained for the utterance. Then, the method determines at least one similarity metric indicative of a degree of similarity between the speech recognition result and the reference information element. A feature vector is determined from the at least one similarity metric, and a score is computed based on the elements of the feature vector. A classifier may be used to process the elements of the feature vector, with the classifier having been trained to tend to produce higher scores when processing training feature vectors derived from utterances known to convey associated reference information elements than when processing training feature vectors derived from utterances known not to convey said associated reference information elements.

Claims

1. A method, comprising:

receiving a speech recognition result derived from ASR processing of a received utterance;

obtaining a reference information element for said utterance;

determining at least one similarity metric indicative of a degree of similarity between said speech recognition result and said reference information element;

determining a score based on said at least one similarity metric;

outputting a data element indicative of said score.

2. The method defined in claim 1, wherein said determining a score comprises:

determining a feature vector from said at least one similarity metric, said feature vector comprising at least one vector element, and

computing said score based from said at least one feature-vector element.

3. The method defined in claim 2, wherein said feature vector comprises a plurality of vector elements.

4. The method defined in claim 3, wherein computing said score comprises processing the plurality of vector elements by a classifier.

5. The method defined in claim 4, said classifier having been trained to tend to produce higher scores when processing training feature vectors derived from utterances known to convey associated reference information elements than when processing training feature vectors derived from utterances known not to convey said associated reference information elements.

6. The method defined in claim 5, wherein said classifier is implemented as a neural network.

7. The method defined in claim 5, wherein said degree of similarity is a function of at least one of a letter insertion cost, a letter deletion cost, a letter substitution cost, a word insertion cost, a word deletion cost and a word substitution cost.

8. The method defined in claim 5, wherein said speech recognition result includes at least one speech recognition hypothesis, wherein said degree of similarity is obtained by performing a dynamic programming alignment between said at least one speech recognition hypothesis and said reference information element.

9. The method defined in claim 5, wherein said speech recognition result includes a plurality of speech recognition hypotheses, wherein said at least one similarity metric comprises a plurality of similarity metrics, each of said plurality of similarity metrics being indicative of a degree of similarity between a respective one of said plurality of speech recognition hypotheses and said reference information element.

10. The method defined in claim 9, wherein at least one of said vector elements is representative of the one of said plurality of similarity metrics that is indicative of the highest degree of similarity.

11. The method defined in claim 9, wherein at least one of said vector elements is representative of an average of said plurality of similarity metrics.

12. The method defined in claim 5, wherein said speech recognition result includes at least one speech recognition hypothesis and further includes, for each of said at least one speech recognition hypothesis, a confidence score associated with the respective speech recognition hypothesis

13. The method defined in claim 12, wherein at least one of said vector elements is determined on a basis of the confidence score associated with each of said at least one speech recognition hypothesis.

14. The method defined in claim 1, further comprising, prior to said receiving said speech recognition result, the step of receiving an identity claim, wherein said obtaining a reference information element for said utterance comprises accessing from a database a record containing a second information element matching the identity claim.

15. The method defined in claim 5, further comprising, prior to said receiving said speech recognition result, the step of receiving an identity claim, wherein said obtaining a reference information element for said utterance comprises accessing from a database a record containing a second information element matching the identity claim.

16. The method defined in claim 15, further comprising:

responsive to said score exceeding a threshold, successfully authenticating the party as having the claimed identity.

17. The method defined in claim 1, further comprising prompting the party to make said utterance.

18. The method defined in claim 17, wherein prompting the party to make said utterance comprises asking the party to respond to a knowledge question.

19. The method defined in claim 18, wherein said knowledge question is associated with a legitimate user having the claimed identity.

20. The method defined in claim 19, further comprising obtaining said knowledge question by accessing a record associated with said legitimate user.

21. The method defined in claim 1, further comprising:

responsive to said score exceeding a threshold, declaring the received utterance as conveying the reference information element.

22. The method defined in claim 21, further comprising:

responsive to said score not exceeding said threshold, declaring the received utterance as not conveying the reference information element.

23. The method defined in claim 1, wherein said at least one speech recognition hypothesis is received from an ASR engine, the method further comprising, prior to said receiving at least one speech recognition hypothesis, the step of providing to the ASR engine a grammar for ASR processing of the utterance received from the party.

24. The method defined in claim 23, further comprising dynamically building said grammar.

25. The method defined in claim 24, wherein dynamically building said grammar is effected on a basis of the reference information element.

26. The method defined in claim 5, further comprising training said classifier.

27. The method defined in claim 26, wherein training said classifier comprises:

providing a plurality of test utterances;

for each test utterance, providing a correct training feature vector and at least one incorrect training feature vector, thereby to create a collection of correct training feature vectors and a collection of incorrect training feature vectors, the correct training feature vector derived from a test utterance known to convey an associated reference information element, the at least one incorrect training feature vector derived from a test utterance known not to convey said associated reference information element;

processing the collection of correct training feature vectors and the collection of incorrect training feature vectors by said classifier while adjusting at least one performance parameter of said classifier and monitoring the score produced by said classifier;

wherein said adjusting is performed to maximize the probability that the score produced by the classifier is greater for the correct training feature vectors in the collection of correct training feature vectors than for the incorrect training feature vectors in the collection of incorrect training feature vectors.

28. The method defined in claim 27, wherein each of said correct training feature vectors is derived from at least one similarity metric computed between (i) an output of ASR processing of the particular test utterance and (ii) said particular reference information element.

29. The method defined in claim 28, wherein each of said incorrect training feature vectors is derived from at least one similarity metric computed between (ii) an output of ASR processing of the particular test utterance and (ii) a reference information element different from said particular reference information element.

30. The method defined in claim 28, wherein said output of ASR processing is derived from ASR processing of said particular test utterance with respect to a grammar that is associated with said particular reference information element.

31. The method defined in claim 30, wherein each of said incorrect training feature vectors is derived from at least one similarity metric computed between (ii) a second output of ASR processing of the particular test utterance and (ii) a reference information element different from said particular reference information element.

32. The method defined in claim 31, wherein said output of ASR processing is derived from ASR processing of said particular test utterance with respect to a grammar that is not associated with said particular reference information element.

33. The method defined in claim 32, further comprising adjusting at least one parameter of said grammar that is associated with said particular reference information element, wherein said adjusting is performed to maximize the probability that the score produced by the classifier is greater for the correct training feature vectors in the collection of correct training feature vectors than for the incorrect training feature vectors in the collection of incorrect training feature vectors.

34. A score computation engine for use in user authentication, comprising:

a feature extractor operable to determine at least one similarity metric indicative of a degree of similarity between (i) a speech recognition result derived from ASR processing of a received utterance; and

(ii) a reference information element for said utterance; and

a classifier operable to determine a score based on said at least one similarity metric and to output a data element indicative of said score.

35. The score computation engine defined in claim 34, wherein said classifier being operable to determine a score comprises said classifier being operable to compute said score from a plurality of feature vector elements of a feature vector determined from said at least one similarity metric.

36. The method defined in claim 35, said classifier having been trained to tend to produce higher scores when processing training feature vectors derived from utterances known to convey associated reference information elements than when processing training feature vectors derived from utterances known not to convey said associated reference information elements.

37. An authentication method, comprising:

receiving from a party a purported identity of a user, the user being associated with a knowledge question and a corresponding stored response to said knowledge question;

providing to the caller an opportunity to respond to said knowledge question associated with the user;

receiving from the caller a first utterance responsive to said providing, said first utterance corresponding to said knowledge question associated with the user;

providing to the caller a second opportunity to respond to said knowledge question associated with the user;

receiving from the caller a plurality of second utterances responsive to said providing, each of said plurality of second utterances corresponding to an alphanumeric character corresponding to said knowledge question associated with the user;

determining a score indicative of a similarity between said plurality of second utterances and the stored response to the knowledge question associated with the user;

declaring the party as either authenticated or not authenticated on the basis of said score.

38. The authentication method defined in claim 37, further comprising:

determining an initial score indicative of a similarity between said first utterance and the stored response to the knowledge question associated with the user;

attempting to authenticate the party on the basis of said initial score;

proceeding with providing to the caller said second opportunity only if said attempting to authenticate is unsuccessful.