System and method of noise-dependent classification

Details System and method of noise-dependent classification System and method of noise-dependent classification

2000-04-27

2003-06-10

Abebe, Daniel (Department: 2654)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S233000, C704S250000

Reexamination Certificate

active

06577997

ABSTRACT:

FIELD OF INVENTION
This invention relates to recognition and more particularly to noise-dependent rejection decision.
BACKGROUND OF INVENTION
Speech recognition is finding more uses today in noisy environments. Speech recognition involves matching acoustic models to input speech and generating probability scores for the match to the models. Simple score-difference-based decision has been reported by C. S. Ramalingam, Y. Gong, L. P. Netsch and Anderson in
Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing,
entitled “Speaker-dependent Name Dialing in a Car Environment,” Phoenix, March 1999. Speech recognition in these noisy environments such as use along a highway can cause severe problems in speech recognition.
SUMMARY OF INVENTION
In accordance with one embodiment of the present invention, improvement is provided by noise-dependent classification of the input that includes detecting noise level and providing a score value based on input recognition. A classifier is responsive to the detected input noise level and the score value for making acceptance or rejection decisions.
In accordance with another embodiment of the present invention, a speech recognition system includes a speech recognizer which has a database of in-vocabulary words that provides a difference score for an utterance relating to in-vocabulary words and a noise detector that detects noise level of the utterance and a classifier responsive to the detected noise level and the difference score for accepting or rejecting the closest in-vocabulary word as a recognized word.


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Yunxin Zhao “Channel identification and signal spectrum estimation for robust A.S.R.” Dec. 1998 IEEE, pp 305-308.

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