IP Library › Granted Patent US 7,774,196
Granted Patent B2
US 7,774,196 · App. 10/953,471 · Granted Aug 10, 2010

System and method for modifying a language model and post-processor information

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Quick Facts
Patent No.
US 7,774,196
App. No.
10/953,471
Granted
Aug 10, 2010
Kind
B2
Abstract

A system and method for automatically modifying a language model and post-processor information is provided. The system includes the steps of language model identification, site-specific model creation, and language model adaptation.

Claims (49)

1. A method in a computer system for selecting a language model, the method comprising:

operating the computer system to perform steps of:

collecting a plurality of language models;

collecting a set of corrected dictated reports;

comparing the corrected dictated reports with the plurality of language models;

determining a perplexity factor for each of the plurality of language models based on the comparison with the corrected dictated reports; and

selecting the one of the plurality of language models having the lowest perplexity factor.

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

collecting site specific data from a user of said computer system; and

creating a site-specific language model by adding the site specific data to the selected one of the plurality of language models having the lowest perplexity factor.

3. The method according to claim 2 , wherein the site specific data comprises add word lists and post processing information.

4. The method according to claim 3 , further comprising adapting the site-specific language model to create a task language model, and outputting the task language model.

5. The method according to claim 4 , wherein adapting the site-specific language model comprises:

updating a set of statistics for the site-specific language model; and

adding an accent word list to the site-specific language model to create the task language model.

6. The method according to claim 5 , wherein the set of statistics includes bigrams or trigrams.

7. The method according to claim 1 , further comprising adapting the selected one of the plurality of language models with the lowest perplexity factor to create a task language model, and outputting the task language model.

8. The method according to claim 7 , wherein adapting the selected one of the plurality of language models with the lowest perplexity factor comprises:

updating a set of statistics for the selected one of the plurality of language models; and

adding an accent word list to the selected one of the plurality of language models to create the task language model.

9. The method according to claim 8 , wherein the set of statistics includes bigrams or trigrams.

10. A method in a computer system comprising:

operating the computer system to perform steps of:

selecting a language model from among a plurality of factory language models;

collecting site specific data from a user;

transforming the selected language model into a site-specific language model by adding the site-specific data to the selected language mode;

determining whether the user has an accent;

creating an accent specific word list based on the user's accent; and

adding the accent specific word list to the site-specific language model.

11. The method according to claim 10 , further comprising;

adapting the site-specific language model to create a task language model; and

outputting the task language model.

12. The method according to claim 11 , wherein adapting the site-specific language model further comprises:

updating a set of statistics for the site-specific language model.

13. The method according to claim 12 , wherein the set of statistics includes bigrams and/or trigrams.

14. A computer system comprising:

at least one computer programmed to perform steps of:

selecting a language model from among a plurality of factory language models;

collecting site specific data from a user;

transforming the selected language model into a site-specific language model by adding the site specific data to the selected language mode;

determining whether the user has an accent;

creating an accent specific word list based on the user's accent; and

adding the accent specific word list to the site-specific language model.

15. The computer system according to claim 14 , wherein the at least one computer is further programmed to:

adapt the site specific language model to create a task language model; and

output the task language model.

16. The computer system according to claim 15 , wherein adapting the site-specific language model further comprises:

updating a set of statistics for the selected language model.

17. The computer system according to claim 16 , wherein the set of statistics includes bigrams or trigrams.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 9, 2023
From: NUANCE COMMUNICATIONS, INC.
To: MICROSOFT TECHNOLOGY LICENSING, LLC
Reel/Frame 065531/0665 →
PATENT RELEASE (REEL:017435/FRAME:0199) Recorded May 20, 2016
From: MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT
To: NUANCE COMMUNICATIONS, INC., AS GRANTOR; ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DELAWARE CORPORATION, AS GRANTOR; SPEECHWORKS INTERNATIONAL, INC., A DELAWARE CORPORATION, AS GRANTOR; TELELOGUE, INC., A DELAWARE CORPORATION, AS GRANTOR; DSP, INC., D/B/A DIAMOND EQUIPMENT, A MAINE CORPORATON, AS GRANTOR; SCANSOFT, INC., A DELAWARE CORPORATION, AS GRANTOR; DICTAPHONE CORPORATION, A DELAWARE CORPORATION, AS GRANTOR
Reel/Frame 038770/0824 →
PATENT RELEASE (REEL:018160/FRAME:0909) Recorded May 20, 2016
From: MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT
To: NUANCE COMMUNICATIONS, INC., AS GRANTOR; ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DELAWARE CORPORATION, AS GRANTOR; SPEECHWORKS INTERNATIONAL, INC., A DELAWARE CORPORATION, AS GRANTOR; TELELOGUE, INC., A DELAWARE CORPORATION, AS GRANTOR; DSP, INC., D/B/A DIAMOND EQUIPMENT, A MAINE CORPORATON, AS GRANTOR; HUMAN CAPITAL RESOURCES, INC., A DELAWARE CORPORATION, AS GRANTOR; INSTITIT KATALIZA IMENI G.K. BORESKOVA SIBIRSKOGO OTDELENIA ROSSIISKOI AKADEMII NAUK, AS GRANTOR; NOKIA CORPORATION, AS GRANTOR; MITSUBISH DENKI KABUSHIKI KAISHA, AS GRANTOR; STRYKER LEIBINGER GMBH & CO., KG, AS GRANTOR; NORTHROP GRUMMAN CORPORATION, A DELAWARE CORPORATION, AS GRANTOR; SCANSOFT, INC., A DELAWARE CORPORATION, AS GRANTOR; DICTAPHONE CORPORATION, A DELAWARE CORPORATION, AS GRANTOR
Reel/Frame 038770/0869 →