TEXT DEPENDENTSPEAKER RECOGNITION WITH LONG-TERM FEATURE BASED ON FUNCTIONAL DATA ANALYSIS
First Claim
1. A method, comprising:
- extracting one or more test features from a time domain signal, wherein the one or more test features are represented by discrete data with a processing system;
representing the discrete data for each of the one or more test features by a corresponding one or more fitting functions with the processing system, wherein each fitting function is defined in terms of a finite number of continuous basis functions and a corresponding finite number of expansion coefficients;
compressing the fitting functions through Functional Principal Component Analysis (FPCA) with the processing system to generate corresponding sets of principal components of the fitting functions for each test feature, wherein each principal component for a given test feature is uncorrelated to each other principal component for the given test feature;
calculating a distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system;
classifying the test feature according to the distance calculated with the processing system; and
adjusting a state of the processing system according to a classification of the test feature determined from the distance calculated.
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Abstract
One or more test features are extracted from a time domain signal. The test features are represented by discrete data. The discrete data is represented for each of the one or more test features by a corresponding one or more fitting functions, which are defined in terms of finite number of continuous basis functions and a corresponding finite number of expansion coefficients. Each fitting function is compressed through Functional Principal Component Analysis (FPCA) to generate corresponding sets of principal components. Each principal component for a given test feature is uncorrelated to each other principal component for the given test feature. A distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system is calculated. The test feature is classified according to the distance calculated with the processing system.
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Citations
13 Claims
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1. A method, comprising:
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extracting one or more test features from a time domain signal, wherein the one or more test features are represented by discrete data with a processing system; representing the discrete data for each of the one or more test features by a corresponding one or more fitting functions with the processing system, wherein each fitting function is defined in terms of a finite number of continuous basis functions and a corresponding finite number of expansion coefficients; compressing the fitting functions through Functional Principal Component Analysis (FPCA) with the processing system to generate corresponding sets of principal components of the fitting functions for each test feature, wherein each principal component for a given test feature is uncorrelated to each other principal component for the given test feature; calculating a distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system; classifying the test feature according to the distance calculated with the processing system; and adjusting a state of the processing system according to a classification of the test feature determined from the distance calculated. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer readable medium having computer-executable instructions embodied therein, the instructions being configured to implement a method comprising:
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extracting one or more test features from a time domain signal, wherein the one or more test features are represented by discrete data with a processing system; representing the discrete data for each of the one or more test features by a corresponding one or more fitting functions with the processing system, wherein each fitting function is defined in terms of a finite number of continuous basis functions and a corresponding finite number of expansion coefficients; compressing the fitting functions through Functional Principal Component Analysis (FPCA) with the processing system to generate corresponding sets of principal components of the fitting functions for each test feature, wherein each principal component for a given test feature is uncorrelated to each other principal component for the given test feature; calculating a distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system; classifying the test feature according to the distance calculated with the processing system; adjusting a state of the processing system according to a classification of the test feature determined from the distance calculated.
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10. A system comprising:
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A processor; A memory; A set of processor-executable instructions embodied in the memory, the instructions being configured to implement a method, the method comprising; extracting one or more test features from a time domain signal, wherein the one or more test features are represented by discrete data with a processing system; representing the discrete data for each of the one or more test features by a corresponding one or more fitting functions with the processing system, wherein each fitting function is defined in terms of a finite number of continuous basis functions and a corresponding finite number of expansion coefficients; compressing the fitting functions through Functional Principal Component Analysis (FPCA) with the processing system to generate corresponding sets of principal components of the fitting functions for each test feature, wherein each principal component for a given test feature is uncorrelated to each other principal component for the given test feature; calculating a distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system; classifying the test feature according to the distance calculated with the processing system; adjusting a state of the processing system according to a classification of the test feature determined from the distance calculated. - View Dependent Claims (11, 12, 13)
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Specification