FREE-SPEECH COMMAND CLASSIFICATION FOR CAR NAVIGATION SYSTEM

• US 20090112605A1
• Filed: 10/27/2008
• Published: 04/30/2009
• Est. Priority Date: 10/26/2007
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Gupta, Rakesh
• Granted Patent: US 8,359,204 B2
• Time in Patent Office: Days
• US Class Current: 704/275
• CPC Class Codes: B60R 16/0373 Voice control in general G10L
• CPC Class Codes: G01C 21/3608 using speech input, e.g. using speech recognition
• CPC Class Codes: G06F 3/167 Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 2015/223 Execution procedure of a spoken command
• CPC Class Codes: G10L 2015/228 of application context

Abstract

The present invention provides a system and method associating the freeform speech commands with one or more predefined commands from a set of predefined commands. The set of predefined commands are stored and alternate forms associated with each predefined command are retrieved from an external data source. The external data source receives the alternate forms associated with each predefined command from multiple sources so the alternate forms represent paraphrases of the predefined command. A representation including words from the predefined command and the alternate forms of the predefined command, such as a vector representation, is generated for each predefined command. A similarity value between received speech data and each representation of a predefined command is computed and the speech data is classified as the predefined command whose representation has the highest similarity value to the speech data.

Claims

1. A computer-implemented method for classifying speech data including one or more words, the method comprising the steps of:

storing a plurality of predefined commands, each predefined command including one or more words and each predefined command associated with an action;

receiving one or more alternate formats associated with each predefined command from a data source, each alternate format including one or more words and an identifier associating the alternate format with a predefined command;

receiving the speech data;

generating a set of values, each value associated with a predefined command and indicating the likelihood that the speech data is associated with the predefined command;

responsive to a value from the set of values, associating the speech data with a predefined command; and

executing the action associated with the predefined command associated with the speech data.

2. The computer-implemented method of claim 1, wherein the data source comprises a website including data obtained from one or more sources using distributed data capture techniques.

3. The computer-implemented method of claim 1, wherein the data source comprises a lexical database including one or more words and one or more synonyms associated with each word.

4. The computer-implemented method of claim 1, wherein the data source comprises a website including data obtained from one or more sources using distributed data capture techniques and a lexical database including one or more words and one or more synonyms associated with each word.

5. The computer-implemented method of claim 1, wherein the step of generating the set of values comprises the steps of:

generating a set of term frequency vectors, wherein each term frequency vector is associated with a predefined command, using the one or more alternate formats associated with each predefined command received from the data source;

generating a term frequency vector associated with the speech data; and

determining a set of similarity values between the term frequency vector associated with the speech data and each term frequency vector associated with a predefined command.

6. The computer-implemented method of claim 5, wherein the similarity value comprises a cosine similarity value between the term frequency vector associated with the speech data and a term frequency vector associated with a predefined command.

7. The computer-implemented method of claim 5, wherein the step of determining the set of similarity values between term frequency vector associated with the speech data and each term frequency vector associated with a predefined command comprises:

applying a Naï

ve Bayes model to the term frequency vector associated with the speech data and the each term frequency vector associated with a predefined command to determine a likelihood of each of predefined command being associated with the speech data.

8. The computer-implemented method of claim 5, wherein the step of determining the set of similarity values between term frequency vector associated with the speech data and each term frequency vector associated with a predefined command comprises:

calculating a probability that each term frequency vector associated with a predefined command includes the term frequency vector associated with the speech data using a unigram model.

9. The computer-implemented method of claim 8, wherein the unigram model mixes a term frequency vector associated with the predefined command and the set of term frequency vectors according to a mixing parameter.

10. The computer-implemented method of claim 5, wherein the step of responsive to a value from the set of values, associating the speech data with a predefined command comprises:

determining a largest similarity value associated with a first predefined command;

determining a second largest similarity value associated with a second predefined command;

calculating a difference between the largest similarity value and the second largest similarity value;

responsive to the difference not exceeding a threshold value, generating a request for an input selecting the first predefined command or the second predefined command; and

associating the speech data with the first predefined command or the second predefined command responsive to the input.

11. The computer-implemented method of claim 10, wherein the step of responsive to a value from the set of values, associating the speech data with a predefined command further comprises:

responsive to the difference exceeding the threshold value, associating the speech data with the first predefined command.

12. A computer program product, comprising a computer readable storage medium storing computer executable code classifying speech data including one or more words, the computer executable code performing the steps of:

storing a plurality of predefined commands, each predefined command including one or more words and each predefined command associated with an action;

receiving one or more alternate formats associated with each predefined command from a data source, each alternate format including one or more words and an identifier associating the alternate format with a predefined command;

receiving the speech data;

generating a set of values, each value associated with a predefined command and indicating the likelihood that the speech data is associated with the predefined command;

responsive to a value from the set of values, associating the speech data with a predefined command; and

executing the action associated with the predefined command associated with the speech data.

13. The computer program product of claim 12, wherein the data source comprises a website including data obtained from one or more sources using distributed data capture techniques.

14. The computer program product of claim 12, wherein the data source comprises a lexical database including one or more words and one or more synonyms associated with each word.

15. The computer program product of claim 12, wherein the data source comprises a website including data obtained from one or more sources using distributed data capture techniques and a lexical database including one or more words and one or more synonyms associated with each word.

16. The computer program product of claim 12, wherein the step of generating the set of values comprises the steps of:

generating a set of term frequency vectors, wherein each term frequency vector is associated with a predefined command, using the one or more alternate formats associated with each predefined command received from the data source;

generating a term frequency vector associated with the speech data; and

determining a set of similarity values between the term frequency vector associated with the speech data and each term frequency vector associated with a predefined command.

17. The computer program product of claim 16, wherein a similarity value comprises a cosine similarity value between the term frequency vector associated with the speech data and a term frequency vector associated with a predefined command.

18. The computer program product of claim 16, wherein the step of determining the set of similarity values between term frequency vector associated with the speech data and each term frequency vector associated with a predefined command comprises:

applying a Naï

ve Bayes model to the term frequency vector associated with the speech data and the each term frequency vector associated with a predefined command to determine a likelihood of each of predefined command being associated with the speech data.

19. The computer program product of claim 16, wherein the step of determining the set of similarity values between term frequency vector associated with the speech data and each term frequency vector associated with a predefined command comprises:

calculating a probability that each term frequency vector associated with a predefined command includes the term frequency vector associated with the speech data using a unigram model.

20. The computer program product of claim 19, wherein the unigram model mixes a term frequency vector associated with the predefined command and the set of term frequency vectors according to a mixing parameter.

21. The computer program product of claim 16, wherein the step of responsive to a value from the set of values, associating the speech data with a predefined command comprises:

determining a largest similarity value associated with a first predefined command;

determining a second largest similarity value associated with a second predefined command;

calculating a difference between the largest similarity value and the second largest similarity value;

responsive to the difference not exceeding a threshold value, generating a request for an input selecting the first predefined command or the second predefined command; and

associating the speech data the first predefined command or the second predefined command responsive to receiving the input.

22. The computer program product of claim 21, wherein the step of responsive to a value from the set of values, associating the speech data with a predefined command further comprises:

responsive to the difference exceeding the threshold value, associating the speech data with the first predefined command.

23. A system for classifying speech data received by a vehicle navigation system comprising:

a predefined command store including a plurality of predefined commands, each predefined command including one or more words and each predefined command associated with an action modifying a vehicle system;

an external data source including one or more alternate formats associated with each predefined command, each alternate format including one or more words and an identifier associating the alternate format with a predefined command;

a speech recognition module for receiving the speech data and converting the speech data into text data; and

an interpretation module coupled to the predefined command store, the external data source and the speech recognition module, the interpretation module receiving the text data, comparing the text data to a representation of each predefined command, the representation of a predefined command including words from the predefined command and words from one or more alternate formats associated with the predefined command, and associating the speech data with a predefined command responsive to the comparison.

24. The system of claim 23, wherein the data source comprises a website including data obtained from one or more sources using distributed data capture techniques.

25. The system of claim 23, wherein the data source comprises a lexical database including one or more words and one or more synonyms associated with each word.

26. The system of claim 23, wherein the data source comprises a website including data obtained from one or more sources using distributed data capture techniques and a lexical database including one or more words and one or more synonyms associated with each word.

27. The system of claim 23, wherein the representation of the predefined command comprises a term frequency vector.