Method and apparatus to detect and delimit foreground speech

• US 6,134,524 A
• Filed: 10/24/1997
• Issued: 10/17/2000
• Est. Priority Date: 10/24/1997
• Status: Expired due to Term

• Current Assignee: Avaya Incorporated
• Original Assignee: Nortel Networks Corporation
• Inventors: Boies, Daniel, Peters, Stephen Douglas
• Primary Examiner(s): ARMSTRONG, ANGELA A
• Application Number:
  US08/950,417
• Time in Patent Office: 1,089 Days
• Field of Search: 704/233, 704/200, 704/201, 704/248, 704/253, 704/231, 704/226, 704/227, 704/228
• US Class Current: 704/233
• CPC Class Codes: G10L 25/87 Detection of discrete points within a voice signal

Abstract

The present invention provides improved foreground-speech signal endpointing by computing a spectral stationarity statistic. This statistic is used by a finite state machine to endpoint speech. Endpointing using the spectral stationarity statistic is less susceptible to background noise than endpointing using conventional measures. The present invention uses frame-synchronous quantile estimation to generate a mask signal for signal to Noise Ratio Normalization.

Claims

1. A method for processing data in a voice recognition system capable of receiving foreground speech in the presence of background noise, comprising the steps, performed by a processor, of

extracting a channel signal;

generating a mask signal from the channel signal;

masking the extracted channel signal with the mask signal; and

taking a sample standard deviation of the masked channel signal over a temporal window; and

generating foreground speech endpoints using the sample standard deviation determined during said taking step.

2. The method of claim 1, wherein the extracting step extracts a channel energy signal.

3. The method of claim 2, further comprising the step of:

performing a background normalization on the sample standard deviation.

4. The method of claim 3, wherein the step of performing background normalization comprises the substeps of:

filtering the masked channel energy signal to produce an estimated background signal; and

subtracting the estimated background signal from the masked channel energy signal.

5. The method of claim 4, wherein the step of filtering comprises the substeps of:

filtering the masked signal using a previous background estimator;

filtering the masked signal using an advanced background estimator; and

selecting the minimum of the filtered masked signals as the estimated background signal.

6. The method of claim 2, wherein generating the mask signal includes the substeps of:

storing a previous mask signal; and

generating the mask signal from the channel signal and the stored previous mask signal.

7. The method of claim 2, further comprising the step of:

computing a high quantile estimation and a low quantile estimation.

8. The method of claim 7, wherein the step of generating the mask signal includes the substep of:

equalizing the separations between the computed high quantile estimate and the extracted channel energy signal and between the computed low quantile estimate and the extracted channel energy signal.

9. The method of claim 2, wherein the step of masking the extracted channel energy signal includes the substep of:

adding the generated mask signal to the extracted channel energy signal.

10. The method of claim 2, further comprising the step of:

smoothing the masked channel energy signal.

11. The method of claim 10, further comprising the step of:

taking a square root of the variance.

12. The method of claim 2, wherein the step of taking the sample standard deviation comprises the substeps of:

storing a plurality of previously taken masked signal values in a buffer;

replacing a least current of the plurality of masked signal values with the current masked signal value; and

computing the sample variance between the plurality of masked signal values stored in the buffer.

13. The method of claim 2, further comprising the step of:

transforming the extracted channel energy signal.

14. The method of claim 13, wherein the transforming step includes taking a generalized logarithm (root) of the extracted channel energy signal.

15. An apparatus in a voice recognition system capable of receiving foreground speech in the presence of background noise, comprising:

means for extracting a channel signal;

means for generating a mask signal from the channel signal;

means for masking the extracted channel signal using the generated mask signal; and

means for taking a sample standard deviation of the masked channel signal over a temporal window, and

means for generating foreground speech endpoints using the sample standard deviation determined by said means for taking.

16. The apparatus of claim 15, wherein the extracting means extracts a channel energy signal.

17. The apparatus of claim 15, further comprising:

means for performing a background normalization on the sample standard deviation.

18. The apparatus of claim 15, further comprising:

a smoothing filter.

19. The apparatus of claim 15, further comprising:

means for computing a high quantile estimate and a low quantile estimate.

20. The apparatus of claim 15, further comprising:

means for generating a background estimate signal; and

means for subtracting the background estimate signal from the sample standard deviation.

21. The apparatus of claim 15, wherein the means for generating a background estimate signal comprises:

a previous background estimator;

an advance background estimator; and

a minimizer to output the minimum of the previous background estimator and the advance background estimator as the background estimate signal.

22. A computer program product comprising:

a computer usable medium having computer readable code embodied therein for processing data in a voice recognition system, the computer usable medium comprising

an extracting module configured to extract a channel energy signal;

a mask generating module configured to generate a mask signal from the channel energy signal;

a masking module configured to mask the extracted channel energy signal with the generated mask signal; and

a standard deviation module configured to take a sample standard deviation of the masked extracted channel energy signal over a temporal window, and

an end point generating module configured to generate foreground speech endpoints using the sample standard deviation determined by said standard deviation module.

23. The computer program product of claim 22, further comprising:

a background normalization module configured to perform background normalization on the sample standard deviation.

24. The computer program product of claim 22, further comprising:

a computing module configured to compute a high quantile estimation and a low quantile estimation.