Concise dynamic grammars using N-best selection

• US 8,682,665 B2
• Filed: 04/28/2011
• Issued: 03/25/2014
• Est. Priority Date: 06/15/1998
• Status: Expired due to Fees

• Current Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: Marcus, Stephen Michael, Rosinski, Richard R., Brown, Deborah W., Goldberg, Randy G.
• Primary Examiner(s): Han, Qi
• Application Number:
  US13/096,431
• Publication Number:
  US 20110202343A1
• Time in Patent Office: 1,062 Days
• Field of Search: 704/243, 704/1, 704/231, 704/235, 704/351, 704/270.1
• US Class Current: 704/243
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/06 Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice G10L15/14 takes precedence
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/228 of application context
• CPC Class Codes: G10L 25/27 characterised by the analysis technique

Abstract

A method and apparatus derive a dynamic grammar composed of a subset of a plurality of data elements that are each associated with one of a plurality of reference identifiers. The present invention generates a set of selection identifiers on the basis of a user-provided first input identifier and determines which of these selection identifiers are present in a set of pre-stored reference identifiers. The present invention creates a dynamic grammar that includes those data elements that are associated with those reference identifiers that are matched to any of the selection identifiers. Based on a user-provided second identifier and on the data elements of the dynamic grammar, the present invention selects one of the reference identifiers in the dynamic grammar.

Claims

1. A method comprising:

creating, via a processor, a correlation table based on speech input from a user, the correlation table comprising alternative character combinations of the speech input, the alternative character combinations comprising groups of characters that are likely to be confused with each other, wherein the speech input comprises a non-alphanumeric character from a set of non-alphanumeric characters comprising (“

#”

, “

$”

, “

@”

, “

%”

, “

˜

”

, and “

_”

);

generating a selection identifier based on the speech input, the selection identifier corresponding to a data element;

comparing the alternative character combinations and the selection identifier to reference identifiers, to yield a matched identifier comprising a reference identifier corresponding to the speech input; and

when the matched identifier comprises more than one reference identifier, prompting the user to select which of the more than one reference identifier corresponds to the speech input.

2. The method of claim 1, further comprising identifying the user based on the reference identifier.

3. The method of claim 1, wherein the correlation table comprising the alternative character combinations of the speech input is based on similar pronunciations of alphanumeric characters.

4. The method of claim 1, wherein the correlation table comprising the alternative character combinations uses a Hidden Markov Model algorithm based on the speech input.

5. The method of claim 1, further comprising analyzing a speech recognition error probability to determine an alternative character combination from the correlation table most likely to match the speech input.

6. The method of claim 5, wherein the speech recognition error probability comprises a likelihood that the speech input was incorrectly recognized by a speech recognizer.

7. The method of claim 1, wherein the reference identifiers correspond to an account number.

8. The method of claim 1, extracting user data from a database, wherein the user data is associated with the reference identifier.

9. A computer-readable device having instructions stored which, when executed by a processor, cause the processor to perform operations comprising:

creating a correlation table based on speech input from a user, the correlation table comprising alternative character combinations of the speech input, the alternative character combinations comprising groups of characters that are likely to be confused with each other, wherein the speech input comprises a non-alphanumeric character from a set of non-alphanumeric characters comprising (“

#”

, “

$”

, “

@”

, “

%”

, “

˜

”

, and “

_”

);

generating a selection identifier based on the speech input, the selection identifier corresponding to a data element;

comparing the alternative character combinations and the selection identifier to reference identifiers, to yield a matched identifier comprising a reference identifier corresponding to the speech input; and

when the matched identifier comprises more than one reference identifier, prompting the user to select which of the more than one reference identifier corresponds to the speech input.

10. The computer-readable device of claim 9, the computer-readable device having additional instructions stored which result in the operations further comprising:

identifying the user based on the reference identifier.

11. The computer-readable device of claim 9, wherein the alternative character combinations are based on similar pronunciations.

12. The computer-readable device of claim 9, the computer-readable device having additional instructions stored which result in the operations further comprising using a Hidden Markov Model to create the correlation table.

13. The computer-readable device of claim 9, the computer-readable device having additional instructions stored which result in the operations further comprising analyzing a likelihood of speech recognition error and selecting an alternative character combination from the correlation table most likely to match the speech input.

14. The computer-readable device of claim 9, the computer-readable device having additional instructions stored which result in the operations further comprising extracting user data from a database, wherein the user data is associated with the reference identifier.

15. A system comprising:

a processor; and

a computer-readable medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising;

creating a correlation table based on speech input from a user, the correlation table comprising alternative character combinations of the speech input, the alternative character combinations comprising groups of characters that are likely to be confused with each other, wherein the speech input comprises a non-alphanumeric character from a set of non-alphanumeric characters comprising (“

#”

, “

$”

, “

@”

, “

%”

, “

˜

”

, and “

_”

);

generating a selection identifier based on the speech input, the selection identifier corresponding to a data element;

comparing the alternative character combinations and the selection identifier to reference identifiers, to yield a matched identifier comprising a reference identifier corresponding to the speech input; and

when the matched identifier comprises more than one reference identifier, prompting the user to select which of the more than one reference identifier corresponds to the speech input.

16. The system of claim 15, the computer-readable medium having additional instructions stored which result in the operations further comprising extracting user data from a database, wherein the user data is associated with the reference identifier.