Method and apparatus for constructing a speech filter using estimates of clean speech and noise

• US 20050182624A1
• Filed: 02/16/2004
• Published: 08/18/2005
• Est. Priority Date: 02/16/2004
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Deng, Li, Wu, Jian, Droppo, James G., Acero, Alejandro
• Granted Patent: US 7,725,314 B2
• Time in Patent Office: Days
• US Class Current: 704/233
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A method and apparatus identify a clean speech signal from a noisy speech signal. To do this, a clean speech value and a noise value are estimated from the noisy speech signal. The clean speech value and the noise value are then used to define a gain on a filter. The noisy speech signal is applied to the filter to produce the clean speech signal. Under some embodiments, the noise value and the clean speech value are used in both the numerator and the denominator of the filter gain, with the numerator being guaranteed to be positive.

Claims

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

receiving an observation vector representing a segment of a noisy speech signal;

estimating a clean speech value and a noise value based on the observation vector;

using the clean speech value and the noise value to set a gain for a filter; and

applying the observation vector to the filter to produce a filtered clean speech vector representing a segment of a clean speech signal.

2. The method of claim 1 wherein estimating a clean speech value and a noise value comprises using parameters that describe a distribution of noise values.

3. The method of claim 2 further comprising determining the parameters of the distribution of noise values.

4. The method of claim 3 wherein determining the parameters of the distribution of noise values comprises determining the parameters based on multiple segments of the noisy speech signal.

5. The method of claim 3 wherein determining the parameters of the distribution of noise values comprises determining a mean of the distribution of noise values using an iteration.

6. The method of claim 5 wherein determining a mean of the distribution of noise values using an iteration comprises at each iteration updating the mean by adding a value to the value of the mean in a past iteration, the value added to the mean not being computed based on a product formed between a covariance of the noise distribution and a difference between the observation vector and another value.

7. The method of claim 1 wherein setting a gain for a filter comprises defining the gain as a ratio with the denominator of the ratio comprising the sum of the clean speech value and the noise value.

8. The method of claim 7 wherein defining the gain as a ratio further comprises defining a ratio with a numerator that is a function of the clean speech value and the noise value.

9. The method of claim 7 wherein defining the gain as a ratio comprises defining the ratio such that it is guaranteed to be positive if the clean speech value and the noise value are positive.

10. The method of claim 1 wherein the observation vector has been formed without applying a frequency-based transform.

11. The method of claim 1 wherein estimating a clean speech value and a noise value comprises using a parameter that describes the covariance of a residue error.

12. The method of claim 11 further comprising determining the covariance of the residue error without using stereo training data.

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

obtaining an estimate of a clean speech value and an estimate of a noise value derived from a noisy speech signal;

setting a numerator of a filter gain ratio as a function of the clean speech value and the noise value;

setting a denominator of the filter gain ratio as a function of the clean speech value and the noise value;

using the filter gain ratio in a filter that is applied to the noisy speech signal.

14. The computer-readable medium of claim 13 wherein obtaining an estimate of a noise value comprises estimating the noise value based in part on a parameter that describes a noise distribution.

15. The computer-readable medium of claim 14 further comprising determining the parameter that describes the noise distribution.

16. The computer-readable medium of claim 15 wherein determining the parameter that describes the noise distribution comprises using the noisy speech signal to determine the parameter.

17. The computer-readable medium of claim 16 wherein determining the parameter comprises determining a mean iteratively, wherein each iteration utilizes an update equation that is formed by maximizing the joint probability of a sequence of observation vectors and a sequence of mixture component indices.

18. The computer-readable medium of claim 13 wherein obtaining an estimate of a clean speech value and an estimate of a noise value comprises estimating a cepstral clean speech value and a cepstral noise value in a cepstral domain and converting the cepstral clean speech value and the cepstral noise value into the spectral domain to produce a spectral domain clean speech value and a spectral domain noise value.

19. The computer-readable medium of claim 18 wherein obtaining an estimate of a clean speech value and an estimate of a noise value further comprises smoothing the spectral domain clean speech value and the spectral domain noise value across frequencies.

20. The computer-readable medium of claim 18 wherein obtaining an estimate of a clean speech value and an estimate of a noise value further comprises smoothing the spectral domain clean speech value and the spectral domain noise value across time.

21. The computer-readable medium of claim 13 wherein obtaining an estimate of the noise value comprises utilizing a parameter that describes a distribution for a residue error.

22. The computer-readable medium of claim 21 further comprising determining the parameter that describes the distribution for the residue error without using clean speech training data.

23. The computer-readable medium of claim 13 wherein setting a numerator comprises setting a numerator such that the numerator is guaranteed to be positive if the clean speech value and the noise value are positive.