Method and apparatus for generating a speech-recognition-based call-routing system

• US 7,206,389 B1
• Filed: 01/07/2004
• Issued: 04/17/2007
• Est. Priority Date: 01/07/2004
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Shahshahani, Ben, Dumoulin, Benoit, Lavoie, Dominic, Tremblay, Real, Kwan, Remi Ken-Sho
• Primary Examiner(s): Gauthier; Gerald
• Application Number:
  US10/753,590
• Time in Patent Office: 1,196 Days
• Field of Search: 379/88.01, 379/88.03, 379/88.04, 704/222, 704/234, 704/256.5
• US Class Current: 379/88.04
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding
• CPC Class Codes: H04M 1/271 controlled by voice recognition
• CPC Class Codes: H04M 2201/40 using speech recognition speech recognition per se G10L15/00
• CPC Class Codes: H04M 3/42 Systems providing special services or facilities to subscribers specially adapted for wireless communication networks H04W4/00

Abstract

A computerized method is provided for electronically directing a call to a class, such that an utterance spoken by a speaker and received by a call-routing system is classified by the call-routing system as being associated with the class, such that the call-routing system includes a speech-recognition module, a feature-extraction module, and a classification module. The method includes extracting features from recognized speech; weighting elements of a feature vector with respective speech-recognition scores, wherein each weighting element is associated with one of the features; ranking classes to which the features are associated; and electronically directing the call to a highest-ranking class.

Claims

1. A computerized method for electronically directing a call to a class, such that an utterance spoken by a speaker and received by a call-routing system is classified by the call-routing system as being associated with the class, such that the call-routing system includes a speech-recognition module, a feature-extraction module, and a classification module, the method comprising:

extracting features from recognized speech;

weighting elements of a feature vector with respective speech-recognition scores, wherein each element is associated with one of the features and each element is a number of times the element'"'"'s associated feature is included in the recognized speech;

ranking classes to which the features are associated based at least in part on the elements of the feature vector; and

electronically directing the call to a highest-ranking class.

2. The method of claim 1, wherein ranking classes includes generating a score for each class and ranking the classes in a ranked list of the classes, and wherein the scores determine the ranking of the classes in the ranked list.

3. The method of claim 1, further comprising generating the recognized speech from the utterance.

4. The method of claim 1, further comprising generating speech-recognition scores for the features.

5. The method of claim 1, wherein generating speech-recognition scores for the features includes generating a confidence score for each word of the recognized speech.

6. The method of claim 5, wherein a confidence score for a feature that includes a phrase is an average of the speech-recognition scores for each word forming the phrase.

7. The method of claim 5, wherein a confidence score for a feature that includes a word is the confidence score for the word.

8. The method of claim 1, wherein the confidence scores are in the range of 0 to 1.

9. A computerized method for electronically directing a call to a class, such that an utterance spoken by a speaker and received by a call-routing system is classified by the call-routing system as being associated with the class, such that the call-routing system includes a speech-recognition module, a feature-extraction module, and a classification module, the method comprising:

extracting sets of features from respective speech-recognition results;

generating a feature vector for each set of features, wherein;

each feature vector includes a set of elements,

each element of a feature vector is associated with a corresponding one of the features in the sets of features associated with the feature vector, and

each element is a number of times the element'"'"'s associated feature is included in an associated speech-recognition result;

weighting elements of each feature vector with respective weighting factors;

summing the feature vectors to generate a final feature vector;

ranking classes associated with the features based on the final feature vector; and

electronically directing the call to a highest-ranking class.

10. The method of claim 9, further comprising deriving the speech-recognition results from the utterance.

11. The method of claim 10, wherein the speech-recognition results are generated by the speech-recognition module.

12. The method of claim 9, wherein the speech-recognition results are a set of nbest speech-recognition results.

13. The method of claim 9, further comprising generating the weighting factors for the features.

14. The method of claim 9, further comprising deriving the weighting factors from speech-recognition scores for words in the recognized speech results.

15. The method of claim 14, wherein each weighting factor is generated from the equation

16. The method of claim 9, wherein an acoustic-confidences score for a feature that includes a word is the speech-recognition score for the word.

17. The method of claim 16, wherein in the speech-recognition scores are in the range of 0 to 1.

18. A computerized method for electronically directing a call to a class, such that an utterance spoken by a speaker and received by a call-routing system is classified by the call-routing system as being associated with the class, such that the call-routing system includes a speech-recognition module, a feature-extraction module, and a classification module, the method comprising:

extracting sets of features from respective speech-recognition results;

generating a feature vector for each set of features, wherein;

each feature vector includes a set of elements,

each element of a feature vector is associated with a corresponding one of the features in the sets of features associated with the feature vector, and

each element is a number of times the element'"'"'s associated feature is included in an associated speech-recognition result;

generating a ranked list for each feature vector based at least in part on the elements of each feature vector;

combining the ranked lists; and

electronically directing the call to a highest scoring class.

19. The method of claim 18, wherein the speech-recognition results are generated by the speech-recognition module based on the utterance.

20. The method of claim 18, wherein the speech-recognition results include a set of nbest speech-recognition results.

21. The method of claim 18, further comprising generating a classifier score for each class in each ranked list.

22. The method of claim 21, further comprising weighting each classifier score with a speech-recognition score.

23. The method of claim 18, wherein combining the ranked lists includes generating final-classifier scores, and wherein the highest scoring class has a highest-final-classifier score.

24. The method of claim 23, wherein the final-classifier scores are derived from the formula:

final_classifier score(class_—

i)=Σ

_j=1ⁿ classifier_score_—

j(class_—

i)^α

·

accoustic_confidence_score_—

j^β

,

wherein class_i is a given class for which a final-classifier score is calculated, the sum is over nbest sequences of words, the classifier_score_j(class_i) is a classifier score for a class associated with a jth nbest sequence of words, and the acoustic_confidence_score_j is the speech-recognition score for the jth nbest sequence of words, and α and

β

are tunable parameter used to place emphasis on the classifier_scores_j or the acoustic_confidence_scores_j.

25. A call-routing system for electronically directing a call to a class, the system comprising:

a speech-recognition module configured to generate nbest sequences of words based on an utterance spoken by a caller;

a feature-extraction module configured to generate features vectors for the nbest sequences of words, wherein;

each feature vector includes a set of elements,

each element of a feature vector is associated with a corresponding one of the features of the feature vector, and

each element is a number of times the element'"'"'s associated feature is included in an associated speech-recognition result in the nbest sequences of words; and

a classification module configured to form a ranked list of classes for the feature vectors based at least in part on the elements of each feature vector, and direct the call to a highest-ranking class in the ranked list of classes.

26. The system of claim 25, wherein the classification module is further configured to generate a set of ranked lists for the feature vectors, and wherein the first mentioned ranked list is a summation of the ranked lists of the set of ranked lists.

27. The system of claim 26, wherein the classification module is configured to weight the ranked lists of the set of ranked lists with weighting factors.

28. The system of claim 27, wherein the weighting factors are calculated from the equation

29. The system of claim 25, wherein the feature-extraction module is configured to weight elements of the feature vectors with acoustic-confidences scores and sum the feature vectors to form a final feature vector.

30. The system of claim 29, wherein the classification module is configured to form the ranked list from the final feature vector.