System and method of providing a spoken dialog interface to a website

• US 8,249,879 B2
• Filed: 11/07/2011
• Issued: 08/21/2012
• Est. Priority Date: 12/18/2002
• Status: Expired due to Term

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: AT&T Intellectual Property II LP (AT&T, Inc.)
• Inventors: Bangalore, Srinivas, Feng, Junlan, Rahim, Mazin G.
• Primary Examiner(s): Smits, Talivaldis Ivars
• Application Number:
  US13/290,501
• Publication Number:
  US 20120053930A1
• Time in Patent Office: 288 Days
• Field of Search: None
• US Class Current: 704/270.1
• CPC Class Codes: G10L 13/08 Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: H04M 3/4936 Speech interaction details speech recognition per se G10L15/00

Abstract

Disclosed is a system and method for training a spoken dialog service component from website data. Spoken dialog service components typically include an automatic speech recognition module, a language understanding module, a dialog management module, a language generation module and a text-to-speech module. The method includes converting data from a structured database associated with a website to a structured text data set and a structured task knowledge base, extracting linguistic items from the structured database, and training a spoken dialog service component using at least one of the structured text data, the structured task knowledge base, or the linguistic items. The system includes modules configured to implement the method.

Claims

1. A method comprising:

translating, via a processor, data associated with a website into a structured text data set and a structured task knowledge base;

extracting from the website a linguistic item; and

combining the linguistic item, the structured task knowledge base, and the structured text data set with a generalized language model, to yield a website specific language model.

2. The method of claim 1, wherein the linguistic item comprises one of a named-entity, a nominal phrase, a verbal phrase, and an adjectival phrase.

3. The method of claim 1, wherein the structured text data set comprises a hierarchical tree.

4. The method of claim 3, wherein the hierarchical tree comprises a plurality of non-leaf tree nodes having a node alias that is human comprehendible.

5. The method of claim 4, wherein each non-leaf tree node further comprises a verbose description document and a concise summary.

6. The method of claim 5, wherein the verbose description document and concise summary are used for information matching and help prompt construction during dialog execution.

7. The method of claim 1, further comprising encoding each webpage in the website as a sequence of feature vectors.

8. A system comprising:

a processor; and

a non-transitory storage device storing instructions for controlling the processor to perform steps comprising;

translating data associated with a website into a structured text data set and a structured task knowledge base;

extracting from the website a linguistic item; and

combining the linguistic item, the structured task knowledge base, and the structured text data set with a generalized language model, to yield a website specific language model.

9. The system of claim 8, wherein the linguistic item comprises one of a named-entity, a nominal phrase, a verbal phrase, and an adjectival phrase.

10. The system of claim 8, wherein the structured text data set comprises a hierarchical tree.

11. The system of claim 10, wherein the hierarchical tree comprises a plurality of non-leaf tree nodes having a node alias that is human comprehendible.

12. The system of claim 11, wherein each non-leaf tree node further comprises a verbose description document and a concise summary.

13. The system of claim 12, wherein the verbose description document and concise summary are used for information matching and help prompt construction during dialog execution.

14. The system of claim 8, wherein the non-transitory storage medium stores further instructions comprising encoding each webpage in the website as a sequence of feature vectors.

15. A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform steps comprising:

translating data associated with a website into a structured text data set and a structured task knowledge base;

extracting from the website a linguistic item; and

combining the linguistic item, the structured task knowledge base, and the structured text data set with a generalized language model, to yield a website specific language model.

16. The non-transitory computer-readable storage medium of claim 15, wherein the linguistic item comprises one of a named-entity, a nominal phrase, a verbal phrase, and an adjectival phrase.

17. The non-transitory computer-readable storage medium of claim 15, wherein the structured text data set comprises a hierarchical tree.

18. The non-transitory computer-readable storage medium of claim 17, wherein the hierarchical tree comprises a plurality of non-leaf tree nodes having a node alias that is human comprehendible.

19. The non-transitory computer-readable storage medium of claim 18, wherein each non-leaf tree node further comprises a verbose description document and a concise summary.

20. The non-transitory computer-readable storage medium of claim 19, wherein the verbose description document and concise summary are used for information matching and help prompt construction during dialog execution.