Speech recognition using channel verification

• US 20070239448A1
• Filed: 03/31/2006
• Published: 10/11/2007
• Est. Priority Date: 03/31/2006
• Status: Active Grant

• Current Assignee: Cerence Operating Company (Cerence Inc.)
• Original Assignee: Nuance Communications, Inc. (Microsoft Corporation)
• Inventors: Zlokarnik, Igor
• Granted Patent: US 7,877,255 B2
• Time in Patent Office: Days
• US Class Current: 704/238
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/065 Adaptation
• 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 for automatic speech recognition includes determining for an input signal a plurality scores representative of certainties that the input signal is associated with corresponding states of a speech recognition model, using the speech recognition model and the determined scores to compute an average signal, computing a difference value representative of a difference between the input signal and the average signal, and processing the input signal in accordance with the difference value.

Claims

1. A method for automatic speech recognition, the method comprising:

determining for an input signal a plurality of scores representative of certainties that the input signal is associated with corresponding states of a speech recognition model;

using the speech recognition model and the determined scores to compute an average signal;

computing a difference value representative of a difference between the input signal and the average signal; and

processing the input signal in accordance with the difference value.

2. The method of claim 1, wherein processing the input signal in accordance with the difference value comprises:

performing speech recognition using the input signal if the difference value does not exceed a first predetermined threshold.

3. The method of claim 1, wherein processing the input signal in accordance with the difference value comprises:

adjusting one or more of the plurality of scores based on the difference value; and

using the adjusted scores to update the speech recognition.

4. The method of claim 3, further comprising:

applying to the input signal a cepstrum transformation; and

generating a mel-frequency cepstral representation comprising mel-frequency coefficients from the cepstrum transform representation of the input signal.

5. The method of claim 4, wherein the average signal is represented using a corresponding mel-frequency cepstral representation, and wherein computing the difference value comprises:

computing a channel deviation value based on the difference between the 0^th dimension of the mel-frequency cepstral coefficient of the mel-cepstral frequency representation of input signal and the 0^th dimension mel-frequency cepstral representation of the average signal.

6. The method of claim 1, wherein computing the average signal comprises:

identifying from the plurality of scores a best score;

selecting from the plurality of scores those scores whose corresponding values are within a second predetermined threshold from the value of the best score; and

performing an averaging operation on the observation mean vectors of observation densities associated with the selected scores to obtain the average signal.

7. The method of claim 1, wherein determining the plurality of scores includes:

performing a negative logarithmic operation on each of the certainties associated with the corresponding states.

8. The method of claim 1, wherein the states of the speech recognition model include hidden Markov model states.

9. The method of claim 1, wherein the one or more of the plurality of scores correspond to at least one of:

state of silence, and state of background noise.

10. A computer program product for automatic speech recognition, the computer program product residing on a machine-readable medium for storing computer instructions that, when executed, cause a processor-based machine to:

determine for an input signal a plurality of scores representative of certainties that the input signal is associated with corresponding states of a speech recognition model;

use the speech recognition model and the determined scores to compute an average signal;

compute a difference value representative of a difference between the input signal and the average signal; and

process the input signal in accordance with the difference value.

11. The computer program product of claim 10, wherein the instructions that cause the processor-based machine to process the input signal in accordance with the difference value comprise instructions that, when executed, cause the processor-based machine to:

perform speech recognition using the input signal if the difference value does not exceed a first predetermined threshold.

12. The computer program product of claim 10, wherein the instructions that cause the processor-based machine to process the input signal in accordance with the difference value comprise instructions that, when executed, cause the processor-based machine to:

adjust one or more of the plurality of scores based on the difference value; and

use the adjusted scores to update the speech recognition.

13. The computer program product of claim 12, further comprising instructions that when executed cause the processor-based machine to:

apply to the input signal a cepstrum transformation; and

generate a mel-frequency cepstral representation comprising mel-frequency coefficients from the cepstrum transform representation of the input signal.

14. The computer program product of claim 13, wherein the average signal is represented using a corresponding mel-frequency cepstral representation, and wherein the instructions that that cause the processor-based machine to compute the difference value comprise instructions that when executed cause the processor-based machine to:

compute a channel deviation value based on the difference between the 0^th dimension of the mel-frequency cepstral coefficient of the mel-cepstral frequency representation of input signal and the 0^th dimension mel-frequency cepstral representation of the average signal.

15. The computer program product of claim 10, wherein the instructions that cause the processor-based machine to compute the average signal comprise instructions that, when executed, cause the processor-based machine to:

identify from the plurality of scores a best score;

select from the plurality of scores those scores whose corresponding values are within a second predetermined threshold from the value of the best score; and

perform an averaging operation on the observation mean vectors of observation densities associated with the selected scores to obtain the average signal.

16. The computer program product of claim 10, wherein the instruction that cause the processor-based machine to determine the plurality of scores includes instruction that, when executed, cause the processor-based machine to:

perform a negative logarithmic operation on each of the certainties associated with the corresponding states.

17. The computer program product of claim 10, wherein the states of the speech recognition model include hidden Markov model states.

18. The computer program product of claim 10, wherein the one or more of the plurality of scores correspond to at least one of:

state of silence, and state of background noise.