FEATURE NORMALIZATION FOR SPEECH AND AUDIO PROCESSING
Systems, method, and apparatus for processing a speech utterance or audio record that includes receiving one or more feature vectors characterizing the speech utterance or audio record, each feature vector having a plurality of feature elements, each feature element being associated with a spectral representation of a characteristic of one of a plurality of sequential segments of the speech utterance or audio record; and processing the one or more feature vectors in a rank order filter to obtain one or more normalized feature vectors, each normalized feature vector having a plurality of normalized feature elements corresponding to the plurality of feature elements.
1 . A method for processing a speech utterance or audio record comprising:
receiving one or more feature vectors characterizing the speech utterance or audio record, each feature vector having a plurality of feature elements, each feature element being associated with a spectral representation of a characteristic of one of a plurality of sequential segments of the speech utterance or audio record; and
processing the one or more feature vectors in a rank order filter to obtain one or more normalized feature vectors, each normalized feature vector having a plurality of normalized feature elements corresponding to the plurality of feature elements.
2 . The method of claim 1 , wherein the rank order filter includes a median filter.
3 . The method of claim 1 , wherein processing the one or more feature vectors includes:
sequentially selecting N consecutive feature elements in the feature vector, N being an integer; and
determining an output of each selection of the N consecutive feature elements according to a rank order criterion.
4 . The method of claim 3 , wherein determining the output of each selection includes:
ranking the selected N consecutive feature elements by magnitude; and
identifying a feature element that has the P th largest magnitude among the magnitudes of the N feature elements, P being an integer between 1 and N.
5 . The method of claim 3 , wherein determining the output of each selection includes:
forming a window vector of a plurality of window elements based on the selected N consecutive feature elements and a weight vector W, the weight vector having a plurality of weight elements representing the number of repetitions of the corresponding feature element in the window vector;
ranking the window elements by magnitude; and
identifying a window element that has the P th largest magnitude among the magnitudes of the window elements, P being an integer between 1 and N.
6 . The method of claim 5 , further comprising:
iteratively performing the step of determining the output of each selection to optimize at least one of the N, P and W.
7 . The method of claim 3 , further comprising:
computing the one or more normalized feature vectors by subtracting the outputs of each selection of the N consecutive feature elements from the corresponding feature vector.
8 . A system for feature normalization comprising:
an interface for receiving one or more feature vectors characterizing a speech utterance, each feature vector having a plurality of feature elements, each feature element being associated with a spectral representation of a characteristic of one of a plurality of sequential segments of the speech utterance; and
a processor for applying a rank order filtering technique to process the one or more feature vectors to obtain one or more normalized feature vectors, each normalized feature vector having a plurality of normalized feature elements corresponding to the plurality of feature elements.
9 . The system of claim 8 , wherein the processor includes a median filter.