Pattern recognition training method and apparatus using inserted noise followed by noise reduction
First Claim
1. A method of generating a pattern recognition model, the method comprising:
- introducing additive noise into a training signal, the additive noise being noise that is similar to noise that is anticipated to be present in a test signal during pattern recognition applying at least one noise reduction technique to the training signal to produce pseudo-clean training data; and
constructing an acoustic model of the pattern recognition model based on the pseudo-clean training data.
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Abstract
A method and apparatus for training and using a pattern recognition model are provided. Under the invention, additive noise that matches noise expected in a test signal is included in a training signal. The noisy training signal is passed through one or more noise reduction techniques to produce pseudo-clean training data. The pseudo-clean training data is used to train the pattern recognition model. When the test signal is received, it is passed through the same noise reduction techniques used on the noisy training signal. This produces pseudo-clean test data, which is applied to the pattern recognition model. Under one embodiment, sets of training data are produced with each set containing a different type of noise.
124 Citations
29 Claims
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1. A method of generating a pattern recognition model, the method comprising:
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introducing additive noise into a training signal, the additive noise being noise that is similar to noise that is anticipated to be present in a test signal during pattern recognition applying at least one noise reduction technique to the training signal to produce pseudo-clean training data; and
constructing an acoustic model of the pattern recognition model based on the pseudo-clean training data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A pattern recognition model having acoustic model parameters consistent with a model that has been trained through a process comprising:
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identifying a type of noise that is expected to be present in a test signal from which a pattern is to be recognized;
generating a training signal such that the training signal contains the identified type of noise;
reducing the noise in the training signal to produce training data; and
generating the acoustic model parameters based on the training data. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A pattern recognition system for recognizing patterns in a test signal, the recognition system comprising:
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a pattern recognition model having model parameters formed through a process comprising;
generating a training signal such that the training signal includes a type of noise that is anticipated to be present in the test signal;
reducing the noise in the training signal using a noise reduction technique to produce cleaned training values; and
using the cleaned training values to form the model parameters;
a noise reduction module being receptive of the test signal and being capable of applying the noise reduction technique to the test signal to produce cleaned test values; and
a decoder, receptive of features of the cleaned test values and capable of accessing the pattern recognition model to identify patterns in the test signal based on the cleaned test values. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29)
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Specification