System and method of using existing knowledge to rapidly train automatic speech recognizers

• US 20030200094A1
• Filed: 12/19/2002
• Published: 10/23/2003
• Est. Priority Date: 04/23/2002
• Status: Abandoned Application

• Current Assignee: AT&T Corporation (AT&T, Inc.)
• Original Assignee: AT&T Corporation (AT&T, Inc.)
• Inventors: Riccardi, Giuseppe, Gupta, Narendra K., Rahim, Mazin G.
• Application Number:
  US10/326,691
• Publication Number:
  US 20030200094A1
• Time in Patent Office: Days
• US Class Current: 704/270.1
• CPC Class Codes: G10L 15/063 Training
• 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

Abstract

A method of rapidly training an automatic speech recognizer as part of a spoken dialog system for an enterprise includes extracting information from enterprise emails, web site content, and/or speech or data records of interactions between customers and the enterprise. The method comprises extracting the relevant data to develop a domain-specific language model, generating an acoustic model from non-domain-specific data, combining the domain-specific language model with the non-domain-specific acoustic model to initially deploy the spoken dialog service, and adapting the language models as task-specific data becomes available.

Claims

1. A method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service for the enterprise, the method comprising:

extracting relevant existing data associated with the enterprise;

training grammars by combining stochastic models from the relevant existing data; and

associating the trained grammars with an automatic speech recognizer for the spoken dialog service.

2. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 1, wherein the relevant existing data is email data.

3. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 1, wherein the relevant existing data is web-based data.

4. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 1, wherein the relevant existing data is recycled data.

5. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 1, wherein extracting relevant existing data associated with the enterprise further comprises applying a filter to the relevant existing data.

6. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 5, further comprising parsing the filtered data into utterances.

7. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 1, wherein the spoken dialog service is associated with a particular task.

8. The method of using enterprise data for preparing an automatic speech recognition module for a spoken dialog service of claim 7, wherein extracting relevant data further comprises extracting data associated with the particular task.

9. A method of using information for rapidly training an automatic speech recognizer, the method comprising:

extracting relevant existing data from a web site associated with an enterprise;

based on the extracted web site data, constructing an information retrieval engine to extract data related to the enterprise from non-web site databases; and

training grammars for the automatic speech recognizer using the relevant existing data.

10. The method of claim 9, further comprising, before constructing the information retrieval engine:

extracting relevant existing data from emails associated with the enterprise, wherein the email-associated data and the web site data are both used to construct the information retrieval engine.

11. A method of using information for rapidly training an automatic speech recognizer, the method comprising:

extracting relevant existing data from emails associated with an enterprise;

based on the extracted email data, constructing an information retrieval engine to extract data related to the enterprise from non-web-site databases; and

training grammars for the automatic speech recognizer using the relevant existing data.

12. An automatic speech recognition module for use in a spoken language dialog service for an enterprise, the automatic speech recognition module generated according to the steps of:

extracting relevant existing data associated with the enterprise;

training grammars by combining stochastic models from the relevant existing data; and

associating the trained grammars with an automatic speech recognizer for the spoken dialog service.

13. The automatic speech recognition module of claim 12, wherein the relevant existing data is email data.

14. The automatic speech recognition module of claim 12, wherein the relevant existing data is web-based data.

15. The automatic speech recognition module of claim 12, wherein the relevant existing data is recycled data.

16. The automatic speech recognition module of claim 12, wherein extracting relevant existing data associated with the enterprise further comprises applying a filter to the relevant existing data.

17. The automatic speech recognition module of claim 16, wherein the filtered data is parsed into utterances.

18. The automatic speech recognition module of claim 12, wherein the spoken dialog service is associated with a particular task.

19. The automatic speech recognition module of claim 18, wherein extracting relevant existing data further comprises extracting data associated with the particular task.

20. A method of collecting data for preparing an automatic speech recognition module for a spoken dialog service associated with a particular task associated with an enterprise, the method comprising:

extracting data relevant to the particular task from data previously stored by the enterprise;

training grammars by combining stochastic models from the relevant data; and

associating the trained grammars with an automatic speech recognizer for the spoken dialog service.

21. An automatic speech recognition module within a spoken dialog service trained according to a method of using enterprise data for preparing a spoken dialog service for the enterprise, the method comprising:

extracting relevant data associated with the enterprise;

training grammars by combining stochastic models from the relevant data; and

associating the trained grammars with an automatic speech recognizer for the spoken dialog service.

22. An automatic speech recognition module for use in a spoken language dialog service for an enterprise, the automatic speech recognition module comprising:

a general-purpose acoustic model generated from non-domain-specific data; and

a domain-specific language model, wherein upon initial deployment of the spoken dialog service, the general-purpose acoustic model and the domain-specific language model are combined to form a deployed language model.

23. The automatic speech recognition module of claim 22, wherein after initial deployment of the spoken dialog service, the deployed language model is adapted using task-specific data gathered from the deployed spoken dialog service.

24. A method of using enterprise data for generating an automatic speech recognition module for a spoken dialog service for the enterprise, the method comprising:

developing a domain-specific language model using domain-specific data;

developing a general acoustic model using non-domain-specific data; and

combining the domain-specific language model and the general acoustic model to generate a deployed language model for initially deploying the spoken dialog service.

25. The method of using enterprise data for generating an automatic speech recognition module of claim 24, further comprising:

after initial deployment of the spoken dialog service, adapting the deployed language model using task-specific data that becomes available.

26. The method of using enterprise data for generating an automatic speech recognition module for a spoken dialog service of claim 24, wherein the domain-specific data is email data.

27. The method of using enterprise data for generating an automatic speech recognition module for a spoken dialog service of claim 24, wherein the domain-specific data is web-based data.

28. The method of using enterprise data for generating an automatic speech recognition module for a spoken dialog service of claim 24, wherein the non-domain-specific data is dialog data associated with speech patterns similar to those in the domain.

29. A TTS spoken dialog service for a domain, the spoken dialog service generated according to the steps of

developing a general purpose acoustic model using non-domain-specific data;

and

developing a domain-specific language model, wherein upon initial deployment of the spoken dialog service, the general-purpose acoustic model and the domain-specific language model are combined to form a deployed language model.

30. The TTS spoken dialog service of claim 29, wherein after initial deployment of the spoken dialog service, the deployed language model is adapted using task-specific data gathered from the deployed spoken dialog service.

31. The TTS spoken dialog service of claim 30, wherein the domain-specific data is email data.

32. The TTS spoken dialog service of claim 31, wherein the domain-specific data is web-based data.

33. The TTS spoken dialog service of claim 29, wherein the non-domain-specific data is dialog data associated with speech patterns similar to those in the domain.

34. A spoken dialog service trained according to a method of using enterprise data for preparing a spoken dialog service for the enterprise, the method comprising:

extracting relevant data associated with the enterprise;

training grammars by combining stochastic models from the relevant data; and

associating the trained grammars with an automatic speech recognizer for the spoken dialog service.

35. The spoken dialog service of claim 34, wherein the relevant data associated with the enterprise comprises web-site data.

36. The spoken dialog service of claim 35, wherein the relevant data associated with the enterprise further comprises email data.

37. The spoken dialog service of claim 36, wherein the relevant data associated with the enterprise further comprises a spoken dialog corpus.