Method and apparatus for command recognition using...

Data processing: speech signal processing – linguistics – language – Speech signal processing – Application

Reexamination Certificate

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C704S251000

Reexamination Certificate

active

06208971

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention pertains to pattern recognition. More particularly, this invention relates to command recognition using data-driven semantic inference.
2. Background
As computer systems have evolved, the desire to use such systems for pattern recognition has grown. Typically, the goal of pattern recognition systems is to quickly provide accurate recognition of input patterns. One type of pattern recognition system is a voice recognition system, which attempts to accurately identify a user's speech. Additionally, one type of voice recognition system is a command or control recognition system. Command/control recognition systems allow a user to input voice commands and selections to the system rather than requiring use of a keyboard, mouse, or similar device.
Typically, the goal of command/control recognition systems is to accurately recognize verbal commands identifying system instructions. One solution to providing command/control interpretation systems is to use a finite state grammar model. Such a model requires the system designer to identify many different ways that each command or instruction could be worded (e.g., different ways in which a user could ask the system to identify the current time include “What time is it?”, “What is the time?”, “Tell me the time.”, etc.). Each of these different ways is programmed into the system and, using the finite state grammar model, the particular grammar of each of these different ways the command could be worded is used in identifying the voice command input by a user. However, such systems are not very adaptable because they do not allow for additional ways to word instructions other than what is programmed into the system. Rather, the recognition process is limited by the grammar programmed into the system. Thus, such systems tend to be user-unfriendly because they typically require a user to memorize particular command phrases.
Thus, what is needed is an improved way to recognize commands.
SUMMARY OF THE INVENTION
A method and apparatus for command recognition using data-driven semantic inference is described herein. According to one aspect of the present invention, a method for recognizing a voice command comprises recognizing a sequence of words received as the voice command. Data-driven semantic inference is then used with the recognized sequence of words to recognize the voice command.
According to one aspect of the present invention, a machine-readable medium has stored thereon a plurality of instructions that, when executed by a processor, cause the processor to recognize a voice command by recognizing a sequence of words received as the voice command, and using data-driven semantic inference with the recognized sequence of words to recognize the voice command.
According to one aspect of the present invention, an apparatus for recognizing a voice command includes a speech recognizer and a semantic classifier. The speech recognizer is to recognize a sequence of words received as the voice command, and the semantic classifier to use data-driven semantic inference with the recognized sequence of words to recognize the voice command.


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J. Caminero-Gill, et al., “Data-Driven Discourse Modeling for Sematic Interpreting,” Proc. IEEE ICASSP 96, vol. 1, pp. 401-404, Apr. 1996.*
Jerome R. Bellegarda, “A Multispan Language Modeling Framework for Large Voculary Speech Recognition,” IEEE Trans. Speech and Audio Processing, vol. 6, No. 5, pp. 456-467, Sep. 1998.*
Deerwester, S. et al. “Indexing By Latent Semantic Analysis,”Journal of the American Society for Information Science, Vol. 41, No. 6, Sep. 1990, pp. 391-407.
Jelinek, F., “Self-Organized Language Modeling For Speech Recognition,” Language Processing For Speech Recognition, Continuous Speech Recognition Group, IBM T. J. Watson Research Center, Yorktown Heights, N.Y., pp. 450-506, in Readings in Speech Recognition, ed. by Alex Waibel and Kai Fu Lee, Morgan-Kzufmann, 1990.

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