Method of pattern recognition using noise reduction uncertainty

• US 20060206325A1
• Filed: 05/16/2006
• Published: 09/14/2006
• Est. Priority Date: 05/20/2002
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Deng, Li, Droppo, James G., Acero, Alejandro
• Granted Patent: US 7,460,992 B2
• Time in Patent Office: Days
• US Class Current: 704/233
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A method and apparatus are provided for using the uncertainty of a noise-removal process during pattern recognition. In particular, noise is removed from a representation of a portion of a noisy signal to produce a representation of a cleaned signal. In the meantime, an uncertainty associated with the noise removal is computed and is used with the representation of the cleaned signal to modify a probability for a phonetic state in the recognition system. In particular embodiments, the uncertainty is used to modify a probability distribution, by increasing the variance in each Gaussian distribution by the amount equal to the estimated variance of the cleaned signal, which is used in decoding the phonetic state sequence in a pattern recognition task.

Claims

1. A method of recognizing pattern states from a noisy signal, the method comprising:

removing noise from a representation of a portion of the noisy signal to produce a representation of a portion of a cleaned signal;

identifying an uncertainty associated with removing the noise;

using the uncertainty to adjust a probability distribution to form a modified probability distribution; and

applying the representation of a portion of the cleaned signal to the modified probability distribution to decode a pattern state.

2. The method of claim 1 wherein adjusting a probability distribution comprises adding the uncertainty to a variance of the probability distribution to form the modified probability distribution.

3. The method of claim 1 wherein the representation of a portion of the noisy signal comprises a component of a feature vector representing a frame of the noisy signal and wherein the representation of a portion of the cleaned signal comprises a component of a feature vector representing a frame of the cleaned signal.

4. The method of claim 3 wherein identifying an uncertainty comprises identifying an uncertainty associated with removing noise from the component of the feature vector for the noisy signal to form the component of the feature vector for the cleaned signal.

5. The method of claim 4 wherein using the uncertainty to adjust a probability distribution comprises using the uncertainty to adjust a probability distribution associated with the component of the feature vector.

6. The method of claim 1 wherein removing noise from a representation of a portion of a noisy signal comprises removing noise from a feature vector representing a frame of the noisy signal.

7. The method of claim 6 wherein removing noise from a feature vector comprises:

identifying a mixture component based on the feature vector for the noisy signal;

identifying a correction vector and an error value associated with the correction vector based on the identified mixture component; and

using the correction vector, the error value, and the feature vector for the noisy signal to identify a feature vector for a frame of the cleaned signal.

8. The method of claim 7 wherein identifying an uncertainty comprises utilizing the error value associated with the correction vector to determine the uncertainty.

9. A computer-readable medium having computer-executable instructions for performing steps comprising:

converting a frame of a noisy signal into a feature vector comprising at least two components;

removing noise from a component of the feature vector for the noisy signal to produce a component of a feature vector for a cleaned signal;

identifying an uncertainty associated with removing the noise from the component;

determining a probability component of a probability for a phonetic state by applying the component for the cleaned signal to a distribution defined in part by the uncertainty associated with removing the noise from the component; and

using the probability component to determine the probability of the phonetic state regardless of the value of the uncertainty.

10. The computer-readable medium of claim 9 wherein defining a probability distribution comprises adding the uncertainty to a variance of a probability distribution.

11. The computer-readable medium of claim 9 wherein removing noise comprises:

identifying a correction vector based on the feature vector for the noisy signal; and

using the correction vector and the feature vector for the noisy signal to form the feature vector for the cleaned signal.

12. The computer-readable medium of claim 11 wherein identifying a correction vector comprises identifying a mixture component based on the feature vector for the noisy signal and selecting a correction vector associated with the mixture component.

13. The computer-readable medium of claim 12 wherein identifying an uncertainty comprises:

identifying an error associated with the mixture component; and

utilizing the error to calculate the uncertainty.

14. A computer-readable medium having computer-executable instructions for performing steps comprising:

identifying an uncertainty value for a noise removal process used to form a noise-reduced value; and

utilizing the uncertainty value to define a probability distribution used in a probability calculation to determine the likelihood of a phonetic state based on the noise-reduced value.

15. The computer-readable medium of claim 14 wherein utilizing the uncertainty value to define a probability distribution comprises utilizing the uncertainty value to define a variance for the probability distribution.

16. The computer-readable medium of claim 15 wherein utilizing the uncertainty value comprises utilizing the uncertainty value to define a separate variance for each of a set of possible phonetic states.

17. The computer-readable medium of claim 14 wherein identifying an uncertainty value comprises identifying one of a plurality of mixture components and selecting an uncertainty value associated with the identified mixture component.

18. The computer-readable medium of claim 14 wherein the step of identifying an uncertainty value is performed for each frame of a noisy signal.

19. The computer-readable medium of claim 14 wherein the noise-reduced value is a feature vector.