System and Method for Making a User Dependent Language Model

• US 20100145677A1
• Filed: 03/03/2009
• Published: 06/10/2010
• Est. Priority Date: 12/04/2008
• Status: Abandoned Application

• Current Assignee: Adacel Systems, Inc. (Adacel Technologies Ltd.)
• Original Assignee: Adacel Systems, Inc. (Adacel Technologies Ltd.)
• Inventors: Shu, Chang-Qing
• Application Number:
  US12/396,933
• Publication Number:
  US 20100145677A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing

Abstract

A language model for a speech recognition engine is made based on user-viewed data files. The data files are reviewed and texts are extracted therefrom. The language model is generated based on the extracted texts. Transcriptions of previous user statements are not required. Different weighting factors can be applied to elements of the extracted texts based on the nature of the data files. The weighting factors are then considered during generation of the language model. A user dependent and application independent language model can be created prior to initial use of the speech recognition engine.

Claims

1. A method of making a language model for a speech recognition engine, the method comprising:

reviewing a plurality of user-viewed data files;

extracting texts from the data files;

associating weighting factors with the extracted texts; and

generating a sorted text element list based on the extracted texts and the weighting factors.

2. The method of claim 1, wherein generating the sorted text element list includes:

generating a merged text element list based on the extracted texts;

determining total weighted occurrences for the text elements in the merged text element list; and

sorting the merged text element list based on the total weighted occurrences of the text elements.

3. The method of claim 2, wherein generating the merged text element list based on the extracted texts includes:

generating separate text elements lists from the extracted texts; and

merging the separate text element lists.

4. The method claim 3, wherein generating the separate text element lists includes:

determining occurrences for the text elements in the separate text element lists; and

applying the weighting factors to the occurrences to determine weighted occurrences for the text elements in the separate text element lists.

5. The method of claim 4, wherein determining the total weighted occurrences for the text elements in the merged text element list includes summing the weighted occurrences for the text elements in the separate text element lists.

6. The method of claim 1, wherein associating weighting factors with the extracted texts includes estimating how closely the extracted texts represent user language usage.

7. The method of claim 1, wherein associating weighting factors with the extracted texts includes weighting user-generated extracted texts higher than other extracted texts.

8. The method claim 7, wherein associating weighting factors with the extracted texts includes weighting the user-generated extracted texts from correspondence data files higher than other user-generated extracted texts.

9. The method of claim 7, wherein associating weighting factors with the extracted texts includes weighting the other extracted texts from correspondence data files higher than the other extracted texts not from correspondence data files.

10. The method of claim 9, wherein associating weighting factors with the extracted texts includes weighting the other extracted texts from correspondence data files with user replies higher than the other extracted texts from correspondence data files without user replies.

11. The method of claim 1, wherein reviewing the plurality of user-viewed data files includes reviewing user-viewed data files stored on a user personal computer.

12. The method of claim 1, further comprising:

generating a sorted text sub-element list based on the extracted texts and the weighting factors.

13. The method of claim 12, further comprising generating a temporary text sub-element list from the sorted text element list and selectively eliminating text sub-elements based on a comparison of the temporary text sub-element list and the sorted text sub-element list.

14. The method of claim 13, wherein text sub-elements occurring in both the temporary text sub-element list and the sorted text sub-element list are eliminated unless comparative occurrences are within a predetermined occurrence threshold.

15. The method of claim 1, further comprising associating topic identifications with the extracted texts.

16. The method of claim 1, wherein the language model is made prior to initial use of the speech recognition engine.

17. A method of making a language model for a speech recognition engine, the method comprising:

reviewing a plurality of user-viewed data files, at least one of the data files including non-transcribed text;

extracting texts from the data files; and

generating a merged text element list based on the extracted texts.

18. The method of claim 17, further comprising:

associating weighting factors with the extracted texts; and

sorting the merged text element list based on the weighting factors.

19. The method of claim 18, wherein sorting the merged text element list based on the weighting factors includes:

determining total weighted occurrences for the text elements in the merged text element list; and

sorting the merged text element list based on the total weighted occurrences of the text elements.

20. The method of claim 18, wherein associating weighting factors with the extracted texts includes estimating how closely the extracted texts represent user language usage.

21. The method of claim 18, wherein associating weighting factors with the extracted texts includes weighting user-generated extracted texts higher than other extracted texts.

22. A system for making a language model for a speech recognition engine, the system comprising:

an extraction module configured to review a plurality of user-viewed data files and extract texts therefrom;

a segmentation module configured to generate separate text element lists from the extracted texts;

a merger module configured to generate a merged text element list from the separate text element lists; and

a sorting module configured to generate a sorted text element list based on the merged text element list.

23. The system of claim 22, wherein the extraction module is further configured to associate weighting factors with the extracted texts and the sorting module is further configured to generate the sorted text elements list from the merged text element list based on the weighting factors.

24. The system of claim 22, wherein the extraction module is further configured to associate weighting factors with the extracted texts by estimating how closely the extracted texts represent user language usage.

25. The system of claim 22, wherein the merger module is further configured to determine total weighted occurrences for text elements in the merged text element list.

26. The system of claim 25, wherein the sorting module is further configured to generate the sorted text elements list from the merged text element list based on the weighting factors by sorting the text elements in the merged text element list by the total weighted occurrences.