System and method for adaptive language understanding by computers

• US 20020178005A1
• Filed: 04/16/2002
• Published: 11/28/2002
• Est. Priority Date: 04/18/2001
• Status: Abandoned Application

• Current Assignee: Rutgers University
• Original Assignee: Rutgers University
• Inventors: Flanagan, James L., Dusan, Sorin V.
• Application Number:
  US10/123,296
• Publication Number:
  US 20020178005A1
• Time in Patent Office: Days
• US Class Current: 704/254
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/56 Natural language generation
• CPC Class Codes: G10L 15/18 using natural language modelling
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• 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

Abstract

A system and method are described for adaptive language understanding using multimodal language acquisition in human-computer interaction. Words, phrases, sentences, production rules (syntactic information) as well as their corresponding meanings (semantic information) are stored. New words, phrases, sentences, production rules and their corresponding meanings can be acquired through interaction with users, using different input modalities, such as, speech, typing, pointing, drawing and image capturing. This system therefore acquires language through a natural language and multimodal interaction with users. New language knowledge is acquired in two ways. First, by acquiring new linguistic units, i.e. words or phrases and their corresponding semantics, and second by acquiring new sentences or language rules and their corresponding computer actions. The system represents an adaptive spoken interface capable of interpreting the user'"'"'s spoken commands and sensory inputs and of learning new linguistic concepts and production rules. Such a system and the underlying method can not only be used to build adaptive conversational or dialog systems, but also to build adaptive interactive computer interfaces and operating systems, expert systems and computer games.

Claims

1. A method for adaptive language understanding using multimodal language acquisition, comprising the steps of:

receiving from a user one or more spoken utterances comprising at least one word;

identifying whether said utterance comprises unknown words not included in a database;

requesting the user to provide semantic information for said identified unknown words;

storing the identified unknown word and creating and storing a new semantic object corresponding to the identified unknown word based on the semantic information received from the user through one or more input modalities.

2. The method of claim 1 wherein said utterance comprise a phrase.

3. The method of claim 1, further comprising converting the spoken utterances included in the database into text strings of words.

4. The method of claim 3, further comprising parsing the text strings and performing a semantic interpretation of said spoken utterance included in the database.

5. A method of claim 4, wherein said database is a rule grammar and the semantic interpretation of said spoken utterance is performed based on information stored in a semantic database.

6. The method of claim 3, wherein the said database comprises allowed words, sentences and production rules.

7. The method of claim 6, further comprising comparing the words of the converted text strings from the spoken utterance with the allowed words in the database.

8. The method of claim 6, further comprising identifying the spoken utterance as the unrecognized spoken utterance if the spoken utterance did not match any of the allowed sentences in the database.

9. The method of claim 8, further comprising the converting of the unrecognized spoken utterance into text strings using a dictation grammar and parsing the converted text strings corresponding to the unrecognized spoken utterances not stored in the database.

10. The method of claim 9, wherein the dictation grammar comprises a vocabulary of words and allows unconstrained utterances.

11. The method of claim 1, further comprising receiving from the user a typed text message including a new sentence or production rule to be recognized along with the corresponding semantics and computer action.

12. The method of claim 1, further comprising indicating to the user via speech to provide the semantic information for the said identified unknown words.

13. The method of claim 1, further comprising storing the identified unknown words into the database after receiving from the user semantic information for the identified unknown words.

14. The method of claim 2, wherein the database represents a context-free grammar organized as a semantic grammar having non-terminal symbols representing semantic classes of concepts.

15. The method of claim 14, wherein the user specifies by voice the concept class from the database to which the identified unknown word or phrase is added after receiving its semantic representation.

16. The method of claim 1, wherein the database is dynamically updated with the new words or phrases after receiving their semantic representation.

17. The method of claim 16, wherein the dynamically updated database can be saved permanently in a file on a hard disk.

18. The method of claim 2, wherein the semantic information of the identified unknown word or phrase is received via devices selected from a group consisting of microphone, keyboard, mouse, pen tablet or video camera, and combinations thereof.

19. The method of claim 18, wherein the user indicates by voice the device that will be used for providing the semantic information for the identified unknown word or phrase.

20. The method of claim 1, further comprising searching for identified unknown words using a parser and comparing each word with all the known words stored in the database.

21. The method of claim 5, wherein the semantic information of the identified unknown word or phrase and the corresponding semantic object are stored in the rule grammar and the semantic database, respectively.

22. An adaptive language understanding computer system comprising:

a) an automatic speech recognition engine for converting spoken utterances into text strings

b) a language understanding module for at least processing spoken utterances having;

i) a rule grammar for storing allowed vocabulary of words, sentences and production rules recognized and understood by the system;

ii) a semantic database for storing semantic objects describing semantic representations of the words; and

iii) a first parser for identifying the semantic interpretation of the recognized and understood spoken utterances;

iv) a command processor for executing appropriate commands or computer actions.

c) a new-word detector module for at least processing spoken utterances not allowed by the rule grammar, having;

i) a dictation grammar for storing a vocabulary of words and allowing the speech recognizer to recognize the spoken utterances if the spoken utterances are not allowed in the rule grammar; and

ii) a second parser for identifying words in the spoken utterances not found in the rule grammar as unknown words;

d) a multimodal semantic acquisition module responsive to an input of semantics for the identified unknown words by creating and storing in the semantic database new semantic objects corresponding to the identified unknown words;

e) a dialog processor module for communicating by synthetic voice with the user;

f) one or more input devices selected from a group consisting of microphone, keyboard, mouse, pen tablet and computer video camera, and combinations thereof.

23. The adaptive language understanding computer system of claim 22, wherein the automatic speech recognizer converts the spoken utterances into text strings using a language model derived from the rule grammar, if the spoken utterance is allowed in the rule grammar.

24. The adaptive language understanding computer system of claim 22, wherein the automatic speech recognizer converts the spoken utterances into text strings using a language model derived from the dictation grammar if the spoken utterance is not allowed in the rule grammar.

25. The adaptive language understanding computer system of claim 22, wherein the dialog processor module comprises text-to-speech converter for converting the text strings into voice messages and forwarding these messages to the user.

26. The adaptive language understanding computer system of claim 22, wherein the dialog processor module comprises a dialog history for temporarily storing the last spoken utterances for elliptical inference in solving ambiguities.

27. The adaptive language understanding computer system of claim 22, wherein the rule grammar database is permanently stored in a file on a hard disk from where it is loaded into a RAM computer memory.

28. The adaptive language understanding computer system of claim 27, wherein the semantic database is permanently stored in a file on the hard disk from where it is loaded into the RAM computer memory.

29. The adaptive language understanding computer system of claim 22, wherein the user indicates by voice the input device that will be used to provide the semantics of the identified unknown words.

30. The adaptive language understanding computer system of claim 22, wherein the identified unknown words are understood by the system after their semantics have been provided by the user.

31. The adaptive language understanding computer system of claim 22, wherein a new sentence or production rule typed by the user along with the corresponding semantics and computer action is acquired and stored in the rule grammar and the semantic database, respectively.