Voice activity detection
AssigneeInternational Business Machines Corporation
InventorZica Valsan
Life of this patent
Estimating the term…
Abstract
Discrimination between two classes comprises receiving a set of frames including an input signal and determining at least two different feature vectors for each of the frames. Discrimination between two classes further comprises classifying the two different feature vectors using sets of preclassifiers trained for at least two classes of events and from that classification, and determining values for at least one weighting factor. Discrimination between two classes still further comprises calculating a combined feature vector for each of the received frames by applying the weighting factor to the feature vectors and classifying the combined feature vector for each of the frames by using a set of classifiers trained for at least two classes of events.
Claims15
A method for discriminating between at least two classes of events, the method comprising:
- receiving a set of frames including an input signal;
- determining at least two different feature vectors for each of the frames, wherein a first feature vector of the at least two different feature vectors is based on energy in different frequency bands, and a second feature vector of the at least two different feature vectors is based on an acoustic model;
- preclassifying the at least two different feature vectors using respective sets of preclassifiers trained for the at least two classes of events, wherein the preclassifying occurs separately from a training of the sets of preclassifiers;
- determining at least one distance between outputs of each of the sets of preclassifiers;
- comparing the at least one distance to at least one predefined threshold, wherein the comparing occurs after determining at least one distance between outputs of each of the sets of preclassifiers is performed;
- determining values for at least one weighting factor based on the at least one distance, using a formula dependent on the comparison;
- calculating a combined feature vector for each of the frames by applying the at least one weighting factor to the at least two different feature vectors;
- and
- classifying the combined feature vector using a set of classifiers trained for the at least two classes of events.
A method for training a voice activity detection system, comprising:
- receiving a set of frames including a training signal;
- determining a quality factor for each of the frames;
- labeling the 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 the frames, wherein a first feature vector of the at least two different feature vectors is based on energy in different frequency bands, and a second feature vector of the at least two different feature vectors is based on an acoustic model;
- training respective sets of preclassifiers to classify the at least two different feature vectors using for the at least two classes of events;
- determining at least one distance between outputs of each of the sets of preclassifiers;
- comparing the at least one distance to at least one predefined threshold, wherein the comparing occurs after determining at least one distance between outputs of each of the sets of preclassifiers is performed;
- determining values for at least one weighting factor based on the at least one distance, using a formula dependent on the comparison;
- calculating a combined feature vector for each of the frames by applying the at least one weighting factor to the at least two different feature vectors;
- and
- classifying the combined feature vector using a set of classifiers to classify the combined feature vector into the at least two classes of events.
A computer-readable storage device with an executable program stored thereon, wherein the program instructs a processor to perform:
- receiving a set of frames including an input signal;
- determining at least two different feature vectors for each of the frames, wherein a first feature vector of the at least two different feature vectors is based on energy in different frequency bands, and a second feature vector of the at least two different feature vectors is based on an acoustic model;
- preclassifying the at least two different feature vectors using respective sets of preclassifiers trained for the at least two classes of events, wherein the reclassifying occurs separately from a training of the sets of preclassifiers;
- determining at least one distance between outputs of each of the sets of preclassifiers;
- comparing the at least one distance to at least one predefined threshold, wherein the comparing occurs after determining at least one distance between outputs of each of the sets of preclassifiers is performed;
- determining values for at least one weighting factor based on the at least one distance, using a formula dependent on the comparison;
- calculating a combined feature vector for each of the frames by applying the at least one weighting factor to the at least two different feature vectors;
- and
- classifying the combined feature vector using a set of classifiers trained for the at least two classes of events.
Dependencies are inferred from the claim text.