Method and device for recognizing human intent

• US 20070094022A1
• Filed: 10/20/2005
• Published: 04/26/2007
• Est. Priority Date: 10/20/2005
• Status: Abandoned Application

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Cheng, Yan, Koo, Hahn
• Application Number:
  US11/254,431
• Publication Number:
  US 20070094022A1
• Time in Patent Office: Days
• US Class Current: 704/251
• CPC Class Codes: G06V 30/268 Lexical context
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue

Abstract

A method (300) and apparatus (100) for recognizing human intent includes capabilities of recognizing (305) a sequence of words by a expression recognizer (115), and determining (310) a most likely value of a replacement for a target word in the sequence of words using the target word, a correction model (210), and one or more words in the sequence of words near the target word. The words may be spoken words, handwritten words, or gesture words. In some embodiments, the expression recognizer may be a speaker independent speech recognizer. The correction model includes conditional probabilities for all word values in a vocabulary, given a particular sequence of words being analyzed, including a target word and words near the tarter word.

Claims

1. A method for recognizing human intent, comprising:

recognizing a sequence of words by an expression recognizer;

determining a most likely value of a replacement for a target word in the sequence of words using the target word, a correction model, and one or more words in the sequence of words near the target word.

2. The method according to claim 1, wherein the replacement comprises one of a substitution of a substitute word for the target word, an insertion of an added word after the target word, and a deletion of the target word.

3. The method according to claim 1, wherein the expression recognizer is one of a speech recognizer, a handwriting recognizer, and a gesture recognizer and the target word is, respectively, one of a spoken language word, a written language word, and a gesture word.

4. The method according to claim 1, wherein the expression recognizer is one of a trained expression recognizer, a knowledge based expression recognizer, and an expression recognizer that is a combination of a trained expression recognizer and a knowledge based expression recognizer.

5. The method according to claim 1, wherein the correction model comprises a set of conditional probabilities for the target word, each conditional probability of the set of conditional probabilities comprising a word value from a vocabulary of words, conditioned by a combination of words from the vocabulary that includes the target word and the one or more words in the sequence near the target word.

6. The method according to claim 5, wherein there M unique words in the vocabulary and C conditional probabilities in the set of conditional probabilities for the target word, wherein M is an integer greater than zero, and wherein C≦

2M+1.

7. The method according to claim 6, wherein there are at most M substitution conditional probabilities, M addition conditional probabilities, and 1 deletion conditional probability in the set of conditional probabilities for the target word.

8. The method according to claim 1, wherein the one or more words in the sequence near the target word comprises P words of the sequence directly preceding the target word, and F words of the sequence directly following the target word, wherein P and F are non-negative integers.

9. The method according to claim 1, further comprising:

presenting the most likely value of the replacement;

obtaining a result that is one of a confirmation and a denial that the most likely value of the replacement is an intended value of the target word; and

performing incremental training of the set of conditional probabilities.

10. The method according to claim 9, wherein performing the incremental training comprises recalculating the conditional probabilities of the set of conditional probabilities for the target word, based on a weighting of existing conditional probabilities that is determined by a quantity of previous incremental trainings of the set of conditional probabilities for the target word, and whether the result is a confirmation or denial.

11. The method according to claim 9, wherein performing the incremental training further comprises capturing a human intended replacement when the result is a denial, and wherein the recalculating of the conditional probabilities is further based on the human intended replacement.

12. A electronic device for recognizing human intent, comprising:

a expression recognizer that recognizes a sequence of words; and

a corrector that determines a most likely value of a replacement for a target word in the sequence of words using the target word, a correction model, and one or more words in the sequence of words near the target word.

13. The electronic device according to claim 12, wherein the corrector comprises a correction model comprising a set of conditional probabilities for the target word, each conditional probability of the set of conditional probabilities comprising a word value from a vocabulary of words, conditioned by a combination of words from the vocabulary that includes the target word and the one or more words in the sequence near the target word.

14. The electronic device according to claim 13, wherein the corrector further comprises:

a presenter that presents the most likely value of the replacement;

a input device that obtains a result that is one of a confirmation and a denial that the most likely value of the replacement is an intended value of the target word; and

an incremental trainer that performs incremental training of the set of conditional probabilities.

15. The electronic device according to claim 14, wherein the input device captures a human intended replacement when the result is a denial, and the incremental trainer bases the incremental training of the set of conditional probabilities upon the human intended replacement.