Voice activity detection system and method

• US 20100057453A1
• Filed: 11/16/2006
• Published: 03/04/2010
• Est. Priority Date: 11/16/2006
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Valsan, Zica
• Granted Patent: US 8,311,813 B2
• Time in Patent Office: Days
• US Class Current: 704/232
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10

Abstract

Discrimination between at least two classes of events in an input signal is carried out in the following way. A set of frames containing an input signal is received, and at least two different feature vectors are determined for each of said frames. Said at least two different feature vectors are classified using respective sets of preclassifiers trained for said at least two classes of events. Values for at least one weighting factor are determined based on outputs of said preclassifiers for each of said frames. A combined feature vector is calculated for each of said frames by applying said at least one weighting factor to said at least two different feature vectors. Said combined feature vector is classified using a set of classifiers trained for said at least two classes of events.

Claims

1. A computerized method for discriminating between at least two classes of events, the method comprising the steps of:

receiving a set of frames containing an input signal, determining at least two different feature vectors for each of said frames,

classifying said at least two different feature vectors using respective sets of preclassifiers trained for said at least two classes of events,

determining values for at least one weighting factor based on outputs of said preclassifiers for each of said frames,

calculating a combined feature vector for each of said frames by applying said at least one weighting factor to said at least two different feature vectors, and

classifying said combined feature vector using a set of classifiers trained for said at least two classes of events.

2. The method of claim 1, comprising

determining at least one distance between outputs of each of said sets of preclassifiers, and

determining values for said at least one weighting factor based on said at least one distance.

3. The method of claim 2, comprising

comparing said at least one distance to at least one predefined threshold, and

calculating values for said at least one weighting factor using a formula dependent on said comparison.

4. The method of claim 3, wherein said formula uses at least one of said at least one threshold values as input.

5. The method of claim 2, wherein said at least one distance is based on at least one of the following:

Kullback-Leibler distance, Mahalanobis distance, and Euclidian distance.

6. The method of claim 1, comprising determining an energy-based feature vector for each of said frames.

7. The method of claim 6, wherein said energy-based feature vector is based on at least one of the following:

energy in different frequency bands, log energy, and speech energy contour.

8. The method of claim 1, comprising determining a model-based feature vector for each of said frames.

9. The method of claim 8, wherein said model-based technique is based on at least one of the following:

an acoustic model, neural networks, and hybrid neural networks and hidden Markow model scheme.

10. The method of claim 1, comprising determining for each of said frames a first feature vector based on energy in different frequency bands and a second feature vector based on an acoustic model.

11. The method of claim 10, wherein said acoustic model is one of the following:

a monolingual acoustic model, and a multilingual acoustic model.

12. A computerized method for training a voice activity detection system, comprising

receiving a set of frames containing a training signal,

determining quality factor for each of said frames;

labelling said frames into at least two classes of events based on the content of the training signal,

determining at least two different feature vectors for each of said frames,

training respective sets of preclassifiers to classify said at least two different feature vectors using for said at least two classes of events,

determining values for at least one weighting factor based on outputs of said preclassifiers for each of said frames,

calculating a combined feature vector for each of said frames by applying said at least one weighting factor to said at least two different feature vectors, and

classifying said combined feature vector using a set of classifiers to classify said combined feature vector into said at least two classes of events.

13. The method of claim 12, comprising determining thresholds for distances between outputs of said preclassifiers for determining values for said at least one weighting factor.

14. A voice activity detection system for discriminating between at least two classes of events, the system comprising:

feature vector units for determining at least two different feature vectors for each frame of a set of frames containing an input signal,

sets of preclassifiers trained for said at least two classes of events for classifying said at least two different feature vectors,

a weighting factor value calculator for determining values for at least one weighting factor based on outputs of said preclassifiers for each of said frames,

a combined feature vector calculator for calculating a value for the combined feature vector for each of said frames by applying said at least one weighting factor to said at least two different feature vectors, and

a set of classifiers trained for said at least two classes of events for classifying said combined feature vector.

15. The system of 14, comprising thresholds for distances between outputs of said preclassifiers for determining values for said at least one weighting factor.

16. A computer program product comprising a computer-usable medium and a computer readable program, wherein the computer readable program including data when executed on a data processing system causes the data processing system to perform operations comprising:

receiving a set of frames containing an input signal,

determining at least two different feature vectors for each of said frames,

classifying said at least two different feature vectors using respective sets of preclassifiers trained for said at least two classes of events,

determining values for at least one weighting factor based on outputs of said preclassifiers for each of said frames,

calculating a combined feature vector for each of said frames by applying said at least one weighting factor to said at least two different feature vectors, and

classifying said combined feature vector using a set of classifiers trained for said at least two classes of events.

17. A computer program product of claim 16, wherein the computer readable problem further includes data that cause the data processing system to perform operations comprising:

determining at least one distance between outputs of each of said sets of preclassifiers, and

determining values for said at least one weighting factor based on said at least one distance.

18. A computer program product of claim 17, wherein the computer readable problem further includes data that cause the data processing system to perform operations comprising:

comparing said at least one distance to at least one predefined threshold, and

calculating values for said at least one weighting factor using a formula dependent on said comparison.

19. The computer program product of claim 18, wherein said formula uses at least one of said at least one threshold values as input.

20. The computer program product of claim 17, wherein said at least one distance is based on at least one of the following:

Kullback-Leibler distance, Mahalanobis distance, and Euclidian distance.

21. The computer program product of claim 16, wherein the computer readable problem further includes data that cause the data processing system to perform operations comprising determining an energy-based feature vector for each of said frames.