Free-speech command classification for car navigation system
First Claim
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;
for each predefined command, representing the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“
TFIDF”
) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command;
receiving the speech data;
generating a term frequency vector associated with the speech data;
determining, for each predefined command, the probability that the speech data is associated with the predefined command based on the sparse vector associated with each predefined command and the term frequency vector associated with the speech data;
associating the speech data with a predefined command based on the determined probabilities; and
executing the action associated with the predefined command associated with the speech data.
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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.
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Citations
27 Claims
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1. A computer-implemented method for classifying speech data including one or more words, the method comprising the steps of:
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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; for each predefined command, representing the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“
TFIDF”
) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command;receiving the speech data; generating a term frequency vector associated with the speech data; determining, for each predefined command, the probability that the speech data is associated with the predefined command based on the sparse vector associated with each predefined command and the term frequency vector associated with the speech data; associating the speech data with a predefined command based on the determined probabilities; and executing the action associated with the predefined command associated with the speech data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer program product, comprising a non-transitory computer readable storage medium storing computer executable code classifying speech data including one or more words, the computer executable code performing the steps of:
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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; for each predefined command, representing the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“
TFIDF”
) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command;receiving the speech data; generating a term frequency vector associated with the speech data; determining, for each predefined command, the probability that the speech data is associated with the predefined command based on the sparse vector associated with each predefined command and the term frequency vector associated with the speech data; associating the speech data with a predefined command based on the determined probabilities; and executing the action associated with the predefined command associated with the speech data. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 27)
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23. A system for classifying speech data received by a vehicle navigation system comprising:
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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 sparse vector module configured to, for each predefined command, represent the predefined command with a sparse vector comprising the term frequency-inverse document frequency (“
TFIDF”
) weights for each word of the predefined command and the one or more alternate formats associated with the predefined command;a speech recognition module for receiving the speech data and generating a term frequency vector associated with the speech data; and an interpretation module coupled to the predefined command store, the external data source and the speech recognition module, the interpretation module for receiving the term frequency vector associated with the speech data, determining for each predefined command the probability that the speech data is associated with a predefined command based on the sparse vector associated with the predefined commend and the term frequency vector associated with the speech data, and associating the speech data with a predefined command responsive to the determined probabilities. - View Dependent Claims (24, 25, 26)
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Specification