Automatic improvement of spoken language

• US 7,412,387 B2
• Filed: 01/18/2005
• Issued: 08/12/2008
• Est. Priority Date: 01/18/2005
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Faisman, Alexander, Kanevsky, Dimitri, Sivan, Zohar
• Primary Examiner(s): MCFADDEN, SUSAN IRIS
• Application Number:
  US11/037,750
• Publication Number:
  US 20060161434A1
• Time in Patent Office: 1,302 Days
• Field of Search: 704/257
• US Class Current: 704/257
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 17/00 Speaker identification or verification techniques

Abstract

A method for improving spoken language includes accepting a speech input from by a speaker using a language, identifying the speaker with a predetermined speaker category and correcting an error in the speech input using an error model that is specific to the speaker category.

Claims

1. A method for improving spoken language, comprising:

predefining a plurality of speaker categories having respective specific error models;

accepting a speech input from a speaker using a language;

identifying the speaker with one of the predefined speaker categories; and

correcting an error in the speech input using specific error model of the one of the speaker categories.

2. A method according to claim 1, wherein accepting the speech input comprises converting the speech input to a text input using a speech-to-text converter.

3. The method according to claim 2, wherein correcting the error comprises correcting a semantic error in the text input.

4. The method according to claim 2, wherein correcting the error comprises correcting a syntax error in the text input.

5. The method according to claim 4, wherein correcting the syntax error comprises defining a sequence of states corresponding to context classes of the speech input, and analyzing the sequence in order to correct the error.

6. The method according to claim 2, wherein correcting the error comprises generating an improved text responsively to the text input and to the error.

7. The method according to claim 6, wherein correcting the error comprises generating an improved speech output responsively to the improved text.

8. The method according to claim 6, wherein generating the improved text comprises presenting the improved text to a human operator for verification.

9. The method according to claim 1, wherein identifying the speaker comprises establishing a personal identity of the speaker, and wherein the specific error model comprises a personal error profile of the speaker.

10. The method according to claim 1, wherein the one of the predefined speaker categories comprises a national origin of the speaker, and wherein the specific error model comprises a model of typical errors made by speakers of the national origin.

11. The method according to claim 10, wherein the specific error model is specific to the language.

12. The method according to claim 1, wherein the one of the predefined speaker categories comprises a category of children, and wherein the specific error model comprises a model of typical errors made by children.

13. The method according to claim 1, wherein identifying the speaker comprises determining the one of the predefined speaker categories responsively to the speech input.

14. The method according to claim 1, wherein correcting the error comprises correcting a phonetic error in the speech input, and wherein the specific error model comprises phonetic rules.