Removing noise from feature vectors

• US 20050256706A1
• Filed: 07/20/2005
• Published: 11/17/2005
• Est. Priority Date: 03/20/2001
• Status: Active Grant

• Current Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Original Assignee: Microsoft Corporation
• Inventors: Deng, Li, Acero, Alejandro, Frey, Brendan J.
• Granted Patent: US 7,451,083 B2
• Time in Patent Office: Days
• US Class Current: 704/226
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• 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

Abstract

A method and computer-readable medium are provided for identifying clean signal feature vectors from noisy signal feature vectors. One aspect of the invention includes using an iterative approach to identify the clean signal feature vector. Another aspect of the invention includes using the variance of a set of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors.

Claims

1. A method of identifying a clean signal feature vector from a noisy signal feature vector, the method comprising:

identifying a mixture of distributions that provide prior probabilities for combinations of clean signal feature vectors and obscuring feature vectors;

determining an observation variance to associate with the noisy signal feature vector; and

using the prior probability mixture of distributions and the observation variance to identify the clean signal feature vector.

2. The method of claim 1 wherein determining an observation variance comprises using a closed-form expression to determine the variance.

3. The method of claim 1 wherein determining an observation variance comprises:

generating a feature vector from a clean signal;

generating a feature vector from a noise signal;

generating an observed noisy signal feature vector from a noisy signal that is the combination of at least the clean signal and the noise signal;

calculating a calculated noisy signal feature vector from the feature vector from the clean signal and the feature vector from the noise signal;

comparing the observed noisy signal feature vector to the calculated noisy signal feature vector to determine the variance.

4. The method of claim 3 wherein determining an observation variance further comprises determining a feature vector for channel distortion and wherein calculating a calculated noisy signal feature vector further comprises calculating the calculated noisy signal feature vector in part from the feature vector for channel distortion.

5. The method of claim 1 wherein identifying a mixture of distributions that provide prior probabilities comprises identifying a mixture of distributions for the obscuring feature vectors and a mixture of distributions for the clean signal feature vectors.

6. A method of identifying a clean signal feature vector from a noisy signal feature vector, the method comprising:

accessing a distribution of training feature vectors that represents a prior probability of combinations of training feature vectors;

setting an initial value for a component of a clean signal feature vector;

determining a revised value for the component of the clean signal feature vector based in part on the initial value for the component, the distribution of training feature vectors, and the noisy signal feature vector;

determining whether to accept the revised value as a final value for the component; and

using the final value for the component to identify the clean signal feature vector.

7. The method of claim 6 wherein determining whether to accept the revised value as a final value comprises:

deciding not to accept the revised value as the final value;

determining a further revised value for the component of the clean signal feature vector based in part on the revised value for the component, the distribution of training feature vectors, and the noisy signal feature vector; and

determining whether to accept the further revised value as a final value for the component.

8. The method of claim 6 wherein the steps of the method are repeated for a plurality of distributions of training feature vectors to generate a final value for a plurality of components of the clean signal feature vector.

9. The method of claim 6 wherein determining a revised value comprises determining a revised value based in part on a variance associated with the noisy signal feature vector.

10. The method of claim 6 wherein accessing a distribution comprises accessing a distribution representing a mixture component of a mixture of distributions for noise training feature vectors.

11. The method of claim 10 wherein the mixture component is described in part by a variance.

12. The method of claim 6 wherein accessing a distribution comprises accessing a distribution representing a mixture component of a mixture of distributions for channel distortion training feature vectors.

13. The method of claim 12 wherein the mixture component is described in part by a variance.

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

accessing a noisy signal feature vector;

accessing at least one distribution of training feature vectors;

identifying an initial value for a clean signal feature vector; and

performing iterations to identify a final value for the clean signal feature vector, each iteration performing a calculation based on the noisy signal feature vector, at least one distribution of training feature vectors, and a current value for the clean signal feature vector, the current value for the clean signal feature vector being updated with each iteration.

15. The computer-readable medium of claim 14 performing a calculation at each iteration further comprises performing the calculation based on a variance for the noisy signal feature vector.

16. The computer-readable medium of claim 14 wherein accessing at least one distribution of training feature vectors comprises accessing at least one distribution of training feature vectors that is based on a distribution of noise training feature vectors described in part by a variance.

17. The computer-readable medium of claim 16 wherein the distribution of noise training feature vectors is a mixture component of a mixture of distributions for noise training feature vectors.

18. The computer-readable medium of claim 14 wherein accessing at least one distribution of training feature vectors comprises accessing at least one distribution of training feature vectors that is based on a distribution of channel distortion training feature vectors described in part by a variance.

19. The computer-readable medium of claim 18 wherein the distribution of channel distortion training feature vectors is a mixture component of a mixture of distributions for channel distortion training feature vectors.

20. The computer-readable medium of claim 18 wherein the at least one distribution of channel distortion training feature vectors comprises at least one distribution of channel distortion training feature vectors associated with a first channel and at least one distribution of channel distortion training feature vectors associated with a second channel.